Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.7
2.8
Journals
Healthcare
Special Issues
Artificial Intelligence (AI) and Machine Learning (ML) in Human Health...
share announcement

Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 125916

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Dr. Md Mahmudur Rahmanon

E-Mail Website
Guest Editor
Department of Computer Science, Morgan State University, Baltimore, MD 21251, USA
Interests: computer vision; image processing; information retrieval; machine learning; deep learning; classification, retrieval and interpretation of medical images; medical caption generation; explainable AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ongoing innovations from the field of Artificial Intelligence (AI) and Machine Learning (ML) are playing a powerful role, influencing the lives of millions through health, safety, education, and other opportunities intended to be shared across all segments of society. In healthcare, AI is bringing a paradigm shift powered by the increasing availability of both structured and unstructured data and rapid development of big data analytic methods. These clinical data often exist in but are not limited to the form of demographics, medical notes, electronic recordings from medical devices (sensors), physical examinations, and clinical laboratory and images. Soon, it will be hard to imagine a doctor’s visit or a hospital stay that does not incorporate AI in numerous ways. It is poised to become a transformational force in which fear that AI will replace doctors and clinicians has gradually dissipated. ML algorithms are already allowing humans to gain unprecedented insights into diagnosing diseases based on histopathological examination or medical imaging, spotting malignant tumors in radiological images, detecting malignancy from photographs of skin lesions, discovering new drugs, finding treatment variability and patient outcomes, and also guiding researchers in how to construct cohorts for costly clinical trials.

Due to the unprecedented pandemic situation around the world, society has seen several recent and major breakthroughs in the healthcare and medicine field, such as COVID-19 vaccine discovery where ML-based techniques were massively favored to accelerate the design and production cycle of the vaccine in addition to enforcing effective social distancing and other preventive measures.

The ultimate goal of AI in healthcare is to re-humanize medicine by providing a set of tools to health professionals so that they can focus more on patient care by enabling the 4P model of medicine (predictive, preventive, personalized, and participatory) and therefore patient autonomy.

This Special Issue invites manuscripts (research, review, opinion, and case studies) on AI- and ML-based ongoing progress and related development to influence human health through healthcare systems, industry, technology, and ethical issues.

Research areas may include (but not be limited to) the following:

  • AI/ML is precision medicine
  • Neural networks and deep learning in healthcare
  • Deep learning in electronic health record (HER)
  • ML- and NLP-based understanding of clinical documentation
  • Application of AI/ML in medical imaging
  • AI-based clinical decision support system
  • AI/ML-based screening systems
  • AI therapy apps during the COVID-19 pandemic
  • AI tools in telemedicine and telehealth
  • ML for oncology-oriented image analysis
  • Machine learning bias in healthcare
  • Explainability for AI in healthcare
  • Ethical, legal, and social implications of using AI system in healthcare
  • The future of AI in healthcare

We look forward to receiving your contributions.

Dr. Mahmudur Rahman
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Deep learning
  • Medical imaging
  • Electronic health record (EHR)
  • Precision medicine
  • Personalized healthcare
  • Decision support system
  • AI based screening system
  • NLP in medicine
  • ML bias

Published Papers (30 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

18 pages, 6486 KiB  
Article
Intelligent IoT (IIoT) Device to Identifying Suspected COVID-19 Infections Using Sensor Fusion Algorithm and Real-Time Mask Detection Based on the Enhanced MobileNetV2 Model
by Rupali Kiran Shinde, Md. Shahinur Alam, Seong Gyoon Park, Sang Myeong Park and Nam Kim
Healthcare 2022, 10(3), 454; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare10030454 - 28 Feb 2022
Cited by 13 | Viewed by 4049
Abstract
This paper employs a unique sensor fusion (SF) approach to detect a COVID-19 suspect and the enhanced MobileNetV2 model is used for face mask detection on an Internet-of-Things (IoT) platform. The SF algorithm avoids incorrect predictions of the suspect. Health data are continuously [...] Read more.
This paper employs a unique sensor fusion (SF) approach to detect a COVID-19 suspect and the enhanced MobileNetV2 model is used for face mask detection on an Internet-of-Things (IoT) platform. The SF algorithm avoids incorrect predictions of the suspect. Health data are continuously monitored and recorded on the ThingSpeak cloud server. When a COVID-19 suspect is detected, an emergency email is sent to healthcare personnel with the GPS position of the suspect. A lightweight and fast deep learning model is used to recognize appropriate mask positioning; this restricts virus transmission. When tested with the real-world masked face dataset (RMFD) dataset, the enhanced MobileNetV2 neural network is optimal for Raspberry Pi. Our IoT device and deep learning model are 98.50% (compared to commercial devices) and 99.26% accurate, respectively, and the time required for face mask evaluation is 31.1 milliseconds. The proposed device is useful for remote monitoring of covid patients. Thus, the method will find medical application in the detection of COVID-19-positive patients. The device is also wearable. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

19 pages, 1580 KiB  
Article
Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease
by Ramesh Chandra Poonia, Mukesh Kumar Gupta, Ibrahim Abunadi, Amani Abdulrahman Albraikan, Fahd N. Al-Wesabi, Manar Ahmed Hamza and Tulasi B
Healthcare 2022, 10(2), 371; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare10020371 - 14 Feb 2022
Cited by 28 | Viewed by 3939
Abstract
Kidney disease is a major public health concern that has only recently emerged. Toxins are removed from the body by the kidneys through urine. In the early stages of the condition, the patient has no problems, but recovery is difficult in the later [...] Read more.
Kidney disease is a major public health concern that has only recently emerged. Toxins are removed from the body by the kidneys through urine. In the early stages of the condition, the patient has no problems, but recovery is difficult in the later stages. Doctors must be able to recognize this condition early in order to save the lives of their patients. To detect this illness early on, researchers have used a variety of methods. Prediction analysis based on machine learning has been shown to be more accurate than other methodologies. This research can help us to better understand global disparities in kidney disease, as well as what we can do to address them and coordinate our efforts to achieve global kidney health equity. This study provides an excellent feature-based prediction model for detecting kidney disease. Various machine learning algorithms, including k-nearest neighbors algorithm (KNN), artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and others, as well as Re-cursive Feature Elimination (RFE) and Chi-Square test feature-selection techniques, were used to build and analyze various prediction models on a publicly available dataset of healthy and kidney disease patients. The studies found that a logistic regression-based prediction model with optimal features chosen using the Chi-Square technique had the highest accuracy of 98.75 percent. White Blood Cell Count (Wbcc), Blood Glucose Random (bgr), Blood Urea (Bu), Serum Creatinine (Sc), Packed Cell Volume (Pcv), Albumin (Al), Hemoglobin (Hemo), Age, Sugar (Su), Hypertension (Htn), Diabetes Mellitus (Dm), and Blood Pressure (Bp) are examples of these traits. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

12 pages, 1258 KiB  
Article
A Noninvasive Risk Stratification Tool Build Using an Artificial Intelligence Approach for Colorectal Polyps Based on Annual Checkup Data
by Chieh Lee, Tsung-Hsing Lin, Chen-Ju Lin, Chang-Fu Kuo, Betty Chien-Jung Pai, Hao-Tsai Cheng, Cheng-Chou Lai and Tsung-Hsing Chen
Healthcare 2022, 10(1), 169; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare10010169 - 17 Jan 2022
Cited by 4 | Viewed by 2230
Abstract
Colorectal cancer is the leading cause of cancer-related deaths worldwide, and early detection has proven to be an effective method for reducing mortality. The machine learning method can be implemented to build a noninvasive stratifying tool that helps identify patients with potential colorectal [...] Read more.
Colorectal cancer is the leading cause of cancer-related deaths worldwide, and early detection has proven to be an effective method for reducing mortality. The machine learning method can be implemented to build a noninvasive stratifying tool that helps identify patients with potential colorectal precancerous lesions (polyps). This study aimed to develop a noninvasive risk-stratified tool for colorectal polyps in asymptomatic, healthy participants. A total of 20,129 consecutive asymptomatic patients who underwent a health checkup between January 2005 and August 2007 were recruited. Positive relationships between noninvasive risk factors, such as age, Helicobacter pylori infection, hypertension, gallbladder polyps/stone, and BMI and colorectal polyps were observed (p < 0.0001), regardless of sex, whereas significant findings were noted in men with tooth disease (p = 0.0053). A risk stratification tool was developed, for colorectal polyps, that considers annual checkup results from noninvasive examinations. For the noninvasive stratified tool, the area under the receiver operating characteristic curve (AUC) of obese females (males) aged <50 years was 91% (83%). In elderly patients (>50 years old), the AUCs of the stratifying tools were >85%. Our results indicate that the risk stratification tool can be built by using random forest and serve as an efficient noninvasive tool to identify patients requiring colonoscopy. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

33 pages, 13277 KiB  
Article
Artificial Intelligence Analysis of Gene Expression Predicted the Overall Survival of Mantle Cell Lymphoma and a Large Pan-Cancer Series
by Joaquim Carreras, Naoya Nakamura and Rifat Hamoudi
Healthcare 2022, 10(1), 155; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare10010155 - 14 Jan 2022
Cited by 19 | Viewed by 4097
Abstract
Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and [...] Read more.
Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients’ overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

21 pages, 6031 KiB  
Article
Real-Time Tracking of Human Neck Postures and Movements
by Korupalli V. Rajesh Kumar and Susan Elias
Healthcare 2021, 9(12), 1755; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9121755 - 19 Dec 2021
Cited by 7 | Viewed by 3261
Abstract
Improper neck postures and movements are the major causes of human neck-related musculoskeletal disorders. To monitor, quantify, analyze, and detect the movements, remote and non-invasive based methods are being developed for prevention and rehabilitation. The purpose of this research is to provide a [...] Read more.
Improper neck postures and movements are the major causes of human neck-related musculoskeletal disorders. To monitor, quantify, analyze, and detect the movements, remote and non-invasive based methods are being developed for prevention and rehabilitation. The purpose of this research is to provide a digital platform for analyzing the impact of human neck movements on the neck musculoskeletal system. The secondary objective is to design a rehabilitation monitoring system that brings accountability in the treatment prescribed, which is shown in the use-case model. To record neck movements effectively, a Smart Neckband integrated with the Inertial Measurement Unit (IMU) was designed. The initial task was to find a suitable position to locate the sensors embedded in the Smart Neckband. IMU-based real-world kinematic data were captured from eight research subjects and were used to extract kinetic data from the OpenSim simulation platform. A Random Forest algorithm was trained using the kinetic data to predict the neck movements. The results obtained correlated with the novel idea proposed in this paper of using the hyoid muscles to accurately detect neck postures and movements. The innovative approach of integrating kinematic data and kinetic data for analyzing neck postures and movements has been successfully demonstrated through the efficient application in a rehabilitation use case with about 95% accuracy. This research study presents a robust digital platform for the integration of kinematic and kinetic data that has enabled the design of a context-aware neckband for the support in the treatment of neck musculoskeletal disorders. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

17 pages, 8397 KiB  
Article
Interocular Symmetry Analysis of Corneal Elevation Using the Fellow Eye as the Reference Surface and Machine Learning
by Shiva Mehravaran, Iman Dehzangi and Md Mahmudur Rahman
Healthcare 2021, 9(12), 1738; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9121738 - 16 Dec 2021
Viewed by 2164
Abstract
Unilateral corneal indices and topography maps are routinely used in practice, however, although there is consensus that fellow-eye asymmetry can be clinically significant, symmetry studies are limited to local curvature and single-point thickness or elevation measures. To improve our current practices, there is [...] Read more.
Unilateral corneal indices and topography maps are routinely used in practice, however, although there is consensus that fellow-eye asymmetry can be clinically significant, symmetry studies are limited to local curvature and single-point thickness or elevation measures. To improve our current practices, there is a need to devise algorithms for generating symmetry colormaps, study and categorize their patterns, and develop reference ranges for new global discriminative indices for identifying abnormal corneas. In this work, we test the feasibility of using the fellow eye as the reference surface for studying elevation symmetry throughout the entire corneal surface using 9230 raw Pentacam files from a population-based cohort of 4613 middle-aged adults. The 140 × 140 matrix of anterior elevation data in these files were handled with Python to subtract matrices, create color-coded maps, and engineer features for machine learning. The most common pattern was a monochrome circle (“flat”) denoting excellent mirror symmetry. Other discernible patterns were named “tilt”, “cone”, and “four-leaf”. Clustering was done with different combinations of features and various algorithms using Waikato Environment for Knowledge Analysis (WEKA). Our proposed approach can identify cases that may appear normal in each eye individually but need further testing. This work will be enhanced by including data of posterior elevation, thickness, and common diagnostic indices. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

11 pages, 655 KiB  
Article
Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors
by Zineb Jeddi, Ihsane Gryech, Mounir Ghogho, Maryame EL Hammoumi and Chafiq Mahraoui
Healthcare 2021, 9(11), 1464; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9111464 - 29 Oct 2021
Cited by 5 | Viewed by 2245
Abstract
The prevalence rate for childhood asthma and its associated risk factors vary significantly across countries and regions. In the case of Morocco, the scarcity of available medical data makes scientific research on diseases such as asthma very challenging. In this paper, we build [...] Read more.
The prevalence rate for childhood asthma and its associated risk factors vary significantly across countries and regions. In the case of Morocco, the scarcity of available medical data makes scientific research on diseases such as asthma very challenging. In this paper, we build machine learning models to predict the occurrence of childhood asthma using data from a prospective study of 202 children with and without asthma. The association between different factors and asthma diagnosis is first assessed using a Chi-squared test. Then, predictive models such as logistic regression analysis, decision trees, random forest and support vector machine are used to explore the relationship between childhood asthma and the various risk factors. First, data were pre-processed using a Chi-squared feature selection, 19 out of the 36 factors were found to be significantly associated (p-value < 0.05) with childhood asthma; these include: history of atopic diseases in the family, presence of mites, cold air, strong odors and mold in the child’s environment, mode of birth, breastfeeding and early life habits and exposures. For asthma prediction, random forest yielded the best predictive performance (accuracy = 84.9%), followed by logistic regression (accuracy = 82.57%), support vector machine (accuracy = 82.5%) and decision trees (accuracy = 75.19%). The decision tree model has the advantage of being easily interpreted. This study identified important maternal and prenatal risk factors for childhood asthma, the majority of which are avoidable. Appropriate steps are needed to raise awareness about the prenatal risk factors. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

24 pages, 690 KiB  
Article
Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning
by Jayroop Ramesh, Niha Keeran, Assim Sagahyroon and Fadi Aloul
Healthcare 2021, 9(11), 1450; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9111450 - 27 Oct 2021
Cited by 20 | Viewed by 3136
Abstract
Obstructive sleep apnea (OSA) is a common, chronic, sleep-related breathing disorder characterized by partial or complete airway obstruction in sleep. The gold standard diagnosis method is polysomnography, which estimates disease severity through the Apnea-Hypopnea Index (AHI). However, this is expensive and not widely [...] Read more.
Obstructive sleep apnea (OSA) is a common, chronic, sleep-related breathing disorder characterized by partial or complete airway obstruction in sleep. The gold standard diagnosis method is polysomnography, which estimates disease severity through the Apnea-Hypopnea Index (AHI). However, this is expensive and not widely accessible to the public. For effective screening, this work implements machine learning algorithms for classification of OSA. The model is trained with routinely acquired clinical data of 1479 records from the Wisconsin Sleep Cohort dataset. Extracted features from the electronic health records include patient demographics, laboratory blood reports, physical measurements, habitual sleep history, comorbidities, and general health questionnaire scores. For distinguishing between OSA and non-OSA patients, feature selection methods reveal the primary important predictors as waist-to-height ratio, waist circumference, neck circumference, body-mass index, lipid accumulation product, excessive daytime sleepiness, daily snoring frequency and snoring volume. Optimal hyperparameters were selected using a hybrid tuning method consisting of Bayesian Optimization and Genetic Algorithms through a five-fold cross-validation strategy. Support vector machines achieved the highest evaluation scores with accuracy: 68.06%, sensitivity: 88.76%, specificity: 40.74%, F1-score: 75.96%, PPV: 66.36% and NPV: 73.33%. We conclude that routine clinical data can be useful in prioritization of patient referral for further sleep studies. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

18 pages, 1670 KiB  
Article
Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook
by Afiq Izzudin A. Rahim, Mohd Ismail Ibrahim, Kamarul Imran Musa, Sook-Ling Chua and Najib Majdi Yaacob
Healthcare 2021, 9(10), 1369; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9101369 - 14 Oct 2021
Cited by 10 | Viewed by 5213
Abstract
Social media sites, dubbed patient online reviews (POR), have been proposed as new methods for assessing patient satisfaction and monitoring quality of care. However, the unstructured nature of POR data derived from social media creates a number of challenges. The objectives of this [...] Read more.
Social media sites, dubbed patient online reviews (POR), have been proposed as new methods for assessing patient satisfaction and monitoring quality of care. However, the unstructured nature of POR data derived from social media creates a number of challenges. The objectives of this research were to identify service quality (SERVQUAL) dimensions automatically from hospital Facebook reviews using a machine learning classifier, and to examine their associations with patient dissatisfaction. From January 2017 to December 2019, empirical research was conducted in which POR were gathered from the official Facebook page of Malaysian public hospitals. To find SERVQUAL dimensions in POR, a machine learning topic classification utilising supervised learning was developed, and this study’s objective was established using logistic regression analysis. It was discovered that 73.5% of patients were satisfied with the public hospital service, whereas 26.5% were dissatisfied. SERVQUAL dimensions identified were 13.2% reviews of tangible, 68.9% of reliability, 6.8% of responsiveness, 19.5% of assurance, and 64.3% of empathy. After controlling for hospital variables, all SERVQUAL dimensions except tangible and assurance were shown to be significantly related with patient dissatisfaction (reliability, p < 0.001; responsiveness, p = 0.016; and empathy, p < 0.001). Rural hospitals had a higher probability of patient dissatisfaction (p < 0.001). Therefore, POR, assisted by machine learning technologies, provided a pragmatic and feasible way for capturing patient perceptions of care quality and supplementing conventional patient satisfaction surveys. The findings offer critical information that will assist healthcare authorities in capitalising on POR by monitoring and evaluating the quality of services in real time. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

18 pages, 1548 KiB  
Article
The Application of Projection Word Embeddings on Medical Records Scoring System
by Chin Lin, Yung-Tsai Lee, Feng-Jen Wu, Shing-An Lin, Chia-Jung Hsu, Chia-Cheng Lee, Dung-Jang Tsai and Wen-Hui Fang
Healthcare 2021, 9(10), 1298; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9101298 - 29 Sep 2021
Cited by 1 | Viewed by 1882
Abstract
Medical records scoring is important in a health care system. Artificial intelligence (AI) with projection word embeddings has been validated in its performance disease coding tasks, which maintain the vocabulary diversity of open internet databases and the medical terminology understanding of electronic health [...] Read more.
Medical records scoring is important in a health care system. Artificial intelligence (AI) with projection word embeddings has been validated in its performance disease coding tasks, which maintain the vocabulary diversity of open internet databases and the medical terminology understanding of electronic health records (EHRs). We considered that an AI-enhanced system might be also applied to automatically score medical records. This study aimed to develop a series of deep learning models (DLMs) and validated their performance in medical records scoring task. We also analyzed the practical value of the best model. We used the admission medical records from the Tri-Services General Hospital during January 2016 to May 2020, which were scored by our visiting staffs with different levels from different departments. The medical records were scored ranged 0 to 10. All samples were divided into a training set (n = 74,959) and testing set (n = 152,730) based on time, which were used to train and validate the DLMs, respectively. The mean absolute error (MAE) was used to evaluate each DLM performance. In original AI medical record scoring, the predicted score by BERT architecture is closer to the actual reviewer score than the projection word embedding and LSTM architecture. The original MAE is 0.84 ± 0.27 using the BERT model, and the MAE is 1.00 ± 0.32 using the LSTM model. Linear mixed model can be used to improve the model performance, and the adjusted predicted score was closer compared to the original score. However, the project word embedding with the LSTM model (0.66 ± 0.39) provided better performance compared to BERT (0.70 ± 0.33) after linear mixed model enhancement (p < 0.001). In addition to comparing different architectures to score the medical records, this study further uses a mixed linear model to successfully adjust the AI medical record score to make it closer to the actual physician’s score. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Graphical abstract

17 pages, 1182 KiB  
Article
Forecast of the COVID-19 Epidemic Based on RF-BOA-LightGBM
by Zhe Li and Dehua Hu
Healthcare 2021, 9(9), 1172; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9091172 - 06 Sep 2021
Cited by 3 | Viewed by 2117
Abstract
In this paper, we utilize the Internet big data tool, namely Baidu Index, to predict the development trend of the new coronavirus pneumonia epidemic to obtain further data. By selecting appropriate keywords, we can collect the data of COVID-19 cases in China between [...] Read more.
In this paper, we utilize the Internet big data tool, namely Baidu Index, to predict the development trend of the new coronavirus pneumonia epidemic to obtain further data. By selecting appropriate keywords, we can collect the data of COVID-19 cases in China between 1 January 2020 and 1 April 2020. After preprocessing the data set, the optimal sub-data set can be obtained by using random forest feature selection method. The optimization results of the seven hyperparameters of the LightGBM model by grid search, random search and Bayesian optimization algorithms are compared. The experimental results show that applying the data set obtained from the Baidu Index to the Bayesian-optimized LightGBM model can better predict the growth of the number of patients with new coronary pneumonias, and also help people to make accurate judgments to the development trend of the new coronary pneumonia. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

17 pages, 3501 KiB  
Article
Incorrect Facemask-Wearing Detection Using Convolutional Neural Networks with Transfer Learning
by Jesús Tomás, Albert Rego, Sandra Viciano-Tudela and Jaime Lloret
Healthcare 2021, 9(8), 1050; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9081050 - 16 Aug 2021
Cited by 34 | Viewed by 4480
Abstract
The COVID-19 pandemic has been a worldwide catastrophe. Its impact, not only economically, but also socially and in terms of human lives, was unexpected. Each of the many mechanisms to fight the contagiousness of the illness has been proven to be extremely important. [...] Read more.
The COVID-19 pandemic has been a worldwide catastrophe. Its impact, not only economically, but also socially and in terms of human lives, was unexpected. Each of the many mechanisms to fight the contagiousness of the illness has been proven to be extremely important. One of the most important mechanisms is the use of facemasks. However, the wearing the facemasks incorrectly makes this prevention method useless. Artificial Intelligence (AI) and especially facial recognition techniques can be used to detect misuses and reduce virus transmission, especially indoors. In this paper, we present an intelligent method to automatically detect when facemasks are being worn incorrectly in real-time scenarios. Our proposal uses Convolutional Neural Networks (CNN) with transfer learning to detect not only if a mask is used or not, but also other errors that are usually not taken into account but that may contribute to the virus spreading. The main problem that we have detected is that there is currently no training set for this task. It is for this reason that we have requested the participation of citizens by taking different selfies through an app and placing the mask in different positions. Thus, we have been able to solve this problem. The results show that the accuracy achieved with transfer learning slightly improves the accuracy achieved with convolutional neural networks. Finally, we have also developed an Android-app demo that validates the proposal in real scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

24 pages, 970 KiB  
Article
Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals
by Matthias Klumpp, Marcus Hintze, Milla Immonen, Francisco Ródenas-Rigla, Francesco Pilati, Fernando Aparicio-Martínez, Dilay Çelebi, Thomas Liebig, Mats Jirstrand, Oliver Urbann, Marja Hedman, Jukka A. Lipponen, Silvio Bicciato, Anda-Petronela Radan, Bernardo Valdivieso, Wolfgang Thronicke, Dimitrios Gunopulos and Ricard Delgado-Gonzalo
Healthcare 2021, 9(8), 961; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9080961 - 29 Jul 2021
Cited by 16 | Viewed by 15556
Abstract
The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life [...] Read more.
The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human–computer interaction, data management, and communication in AI implementation projects. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

15 pages, 1162 KiB  
Article
Machine Learning to Predict the Progression of Bone Mass Loss Associated with Personal Characteristics and a Metabolic Syndrome Scoring Index
by Chao-Hsin Cheng, Ching-Yuan Lin, Tsung-Hsun Cho and Chih-Ming Lin
Healthcare 2021, 9(8), 948; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9080948 - 28 Jul 2021
Cited by 5 | Viewed by 2346
Abstract
A relationship exists between metabolic syndrome (MetS) and human bone health; however, whether the combination of demographic, lifestyle, and socioeconomic factors that are associated with MetS development also simultaneously affects bone density remains unclear. Using a machine learning approach, the current study aimed [...] Read more.
A relationship exists between metabolic syndrome (MetS) and human bone health; however, whether the combination of demographic, lifestyle, and socioeconomic factors that are associated with MetS development also simultaneously affects bone density remains unclear. Using a machine learning approach, the current study aimed to estimate the usefulness of predicting bone mass loss using these potentially related factors. The present study included a sample of 23,497 adults who routinely visited a health screening center at a large health center at least once during each of three 3-year stages (i.e., 2006–2008, 2009–2011, and 2012–2014). The demographic, socioeconomic, lifestyle characteristics, body mass index (BMI), and MetS scoring index recorded during the first 3-year stage were used to predict the subsequent occurrence of osteopenia using a non-concurrence design. A concurrent prediction was also performed using the features recorded from the same 3-year stage as the predicted outcome. Machine learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were applied to build predictive models using a unique feature set. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and F1 score were used to evaluate the predictive performances of the models. The XGBoost model presented the best predictive performance among the non-concurrence models. This study suggests that the ensemble learning model with a MetS severity score can be used to predict the progression of osteopenia. The inclusion of an individual’s features into a predictive model over time is suggested for future studies. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

10 pages, 539 KiB  
Article
Signal and Texture Features from T2 Maps for the Prediction of Mild Cognitive Impairment to Alzheimer’s Disease Progression
by Alejandro I. Trejo-Castro, Ricardo A. Caballero-Luna, José A. Garnica-López, Fernando Vega-Lara and Antonio Martinez-Torteya
Healthcare 2021, 9(8), 941; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9080941 - 26 Jul 2021
Cited by 2 | Viewed by 2096
Abstract
Early detection of Alzheimer’s disease (AD) is crucial to preserve cognitive functions and provide the opportunity for patients to enter clinical trials. In recent years, some studies have reported that features related to the signal and texture of MRI images can be an [...] Read more.
Early detection of Alzheimer’s disease (AD) is crucial to preserve cognitive functions and provide the opportunity for patients to enter clinical trials. In recent years, some studies have reported that features related to the signal and texture of MRI images can be an effective biomarker of AD. To test these claims, a study was conducted using T2 maps, a sequence not previously studied, of 40 patients with mild cognitive impairment (MCI) from the Alzheimer’s Disease Neuroimaging Initiative database, who either progressed to AD (18) or remained stable (22). From these maps, the mean value and absolute difference of 37 signal and texture imaging features for 40 contralateral pairs of regions were measured. We used seven machine learning methods to analyze whether, by adding these imaging features to the neuropsychological studies currently used for diagnosis, we could more accurately identify patients who will progress to AD. The predictive models improved with the addition of signal and texture features. Additionally, features related to the signal and texture of the images were much more relevant than volumetric ones. Our results suggest that contralateral signal and texture features should be further investigated as potential biomarkers for the prediction of AD. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

23 pages, 4436 KiB  
Article
Loss Weightings for Improving Imbalanced Brain Structure Segmentation Using Fully Convolutional Networks
by Takaaki Sugino, Toshihiro Kawase, Shinya Onogi, Taichi Kin, Nobuhito Saito and Yoshikazu Nakajima
Healthcare 2021, 9(8), 938; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9080938 - 26 Jul 2021
Cited by 24 | Viewed by 3062
Abstract
Brain structure segmentation on magnetic resonance (MR) images is important for various clinical applications. It has been automatically performed by using fully convolutional networks. However, it suffers from the class imbalance problem. To address this problem, we investigated how loss weighting strategies work [...] Read more.
Brain structure segmentation on magnetic resonance (MR) images is important for various clinical applications. It has been automatically performed by using fully convolutional networks. However, it suffers from the class imbalance problem. To address this problem, we investigated how loss weighting strategies work for brain structure segmentation tasks with different class imbalance situations on MR images. In this study, we adopted segmentation tasks of the cerebrum, cerebellum, brainstem, and blood vessels from MR cisternography and angiography images as the target segmentation tasks. We used a U-net architecture with cross-entropy and Dice loss functions as a baseline and evaluated the effect of the following loss weighting strategies: inverse frequency weighting, median inverse frequency weighting, focal weighting, distance map-based weighting, and distance penalty term-based weighting. In the experiments, the Dice loss function with focal weighting showed the best performance and had a high average Dice score of 92.8% in the binary-class segmentation tasks, while the cross-entropy loss functions with distance map-based weighting achieved the Dice score of up to 93.1% in the multi-class segmentation tasks. The results suggested that the distance map-based and the focal weightings could boost the performance of cross-entropy and Dice loss functions in class imbalanced segmentation tasks, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

16 pages, 708 KiB  
Article
Clusters of Physical Frailty and Cognitive Impairment and Their Associated Comorbidities in Older Primary Care Patients
by Sanja Bekić, František Babič, Viera Pavlišková, Ján Paralič, Thomas Wittlinger and Ljiljana Trtica Majnarić
Healthcare 2021, 9(7), 891; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9070891 - 15 Jul 2021
Cited by 9 | Viewed by 2480
Abstract
(1) Objectives: We aimed to identify clusters of physical frailty and cognitive impairment in a population of older primary care patients and correlate these clusters with their associated comorbidities. (2) Methods: We used a latent class analysis (LCA) as the clustering technique to [...] Read more.
(1) Objectives: We aimed to identify clusters of physical frailty and cognitive impairment in a population of older primary care patients and correlate these clusters with their associated comorbidities. (2) Methods: We used a latent class analysis (LCA) as the clustering technique to separate different stages of mild cognitive impairment (MCI) and physical frailty into clusters; the differences were assessed by using a multinomial logistic regression model. (3) Results: Four clusters (latent classes) were identified: (1) highly functional (the mean and SD of the “frailty” test 0.58 ± 0.72 and the Mini-Mental State Examination (MMSE) test 27.42 ± 1.5), (2) cognitive impairment (0.97 ± 0.78 and 21.94 ± 1.95), (3) cognitive frailty (3.48 ± 1.12 and 19.14 ± 2.30), and (4) physical frailty (3.61 ± 0.77 and 24.89 ± 1.81). (4) Discussion: The comorbidity patterns distinguishing the clusters depend on the degree of development of cardiometabolic disorders in combination with advancing age. The physical frailty phenotype is likely to exist separately from the cognitive frailty phenotype and includes common musculoskeletal diseases. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

29 pages, 34125 KiB  
Article
Visual Algorithm of VR E-Sports for Online Health Care
by Sang-Guk Lim, Se-Hoon Jung and Jun-Ho Huh
Healthcare 2021, 9(7), 824; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9070824 - 29 Jun 2021
Cited by 7 | Viewed by 3327
Abstract
The need for non-face-to-face online health care has emerged through the era of “untact”. However, there is a lack of standardization work and research cases on the exercise effect of immersive content. In this study, the possibility of the exercise effect of VR [...] Read more.
The need for non-face-to-face online health care has emerged through the era of “untact”. However, there is a lack of standardization work and research cases on the exercise effect of immersive content. In this study, the possibility of the exercise effect of VR e-sports among e-sports cases were presented through a visual algorithm analysis. In addition, the evaluation criteria were established. The research method compares and analyzes e-sports cases and VR e-sports cases by applying existing evaluation research cases. It also sets up a new evaluation standard. As for the analysis result, the device immersion method and interaction range were set through an algorithm analysis; FOV and frame immersion were set through typification; the user recognition method and interaction method were set through the visual diagram. Then, each derived result value was quantified and a new evaluation criterion was proposed. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

12 pages, 2750 KiB  
Article
Post-Analysis of Predictive Modeling with an Epidemiological Example
by Christina Brester, Ari Voutilainen, Tomi-Pekka Tuomainen, Jussi Kauhanen and Mikko Kolehmainen
Healthcare 2021, 9(7), 792; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9070792 - 24 Jun 2021
Viewed by 1538
Abstract
Post-analysis of predictive models fosters their application in practice, as domain experts want to understand the logic behind them. In epidemiology, methods explaining sophisticated models facilitate the usage of up-to-date tools, especially in the high-dimensional predictor space. Investigating how model performance varies for [...] Read more.
Post-analysis of predictive models fosters their application in practice, as domain experts want to understand the logic behind them. In epidemiology, methods explaining sophisticated models facilitate the usage of up-to-date tools, especially in the high-dimensional predictor space. Investigating how model performance varies for subjects with different conditions is one of the important parts of post-analysis. This paper presents a model-independent approach for post-analysis, aiming to reveal those subjects’ conditions that lead to low or high model performance, compared to the average level on the whole sample. Conditions of interest are presented in the form of rules generated by a multi-objective evolutionary algorithm (MOGA). In this study, Lasso logistic regression (LLR) was trained to predict cardiovascular death by 2016 using the data from the 1984–1989 examination within the Kuopio Ischemic Heart Disease Risk Factor Study (KIHD), which contained 2682 subjects and 950 preselected predictors. After 50 independent runs of five-fold cross-validation, the model performance collected for each subject was used to generate rules describing “easy” and “difficult” cases. LLR with 61 selected predictors, on average, achieved 72.53% accuracy on the whole sample. However, during post-analysis, three categories of subjects were discovered: “Easy” cases with an LLR accuracy of 95.84%, “difficult” cases with an LLR accuracy of 48.11%, and the remaining cases with an LLR accuracy of 71.00%. Moreover, the rule analysis showed that medication was one of the main confusing factors that led to lower model performance. The proposed approach provides insightful information about subjects’ conditions that complicate predictive modeling. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

13 pages, 770 KiB  
Article
The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients
by Yen-Chun Huang, Shao-Jung Li, Mingchih Chen and Tian-Shyug Lee
Healthcare 2021, 9(6), 710; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9060710 - 10 Jun 2021
Cited by 8 | Viewed by 2602
Abstract
Most patients face expensive healthcare management after coronary artery bypass grafting (CABG) surgery, which brings a substantial financial burden to the government. The National Health Insurance Research Database (NHIRD) is a complete database containing over 99% of individuals’ medical information in Taiwan. Our [...] Read more.
Most patients face expensive healthcare management after coronary artery bypass grafting (CABG) surgery, which brings a substantial financial burden to the government. The National Health Insurance Research Database (NHIRD) is a complete database containing over 99% of individuals’ medical information in Taiwan. Our research used the latest data that selected patients who accepted their first CABG surgery between January 2014 and December 2017 (n = 12,945) to predict which factors will affect medical expenses, and built the prediction model using different machine learning algorithms. After analysis, our result showed that the surgical expenditure (X4) and 1-year medical expenditure before the CABG operation (X14), and the number of hemodialysis (X15), were the key factors affecting the 1-year medical expenses of CABG patients after discharge. Furthermore, the XGBoost and SVR methods are both the best predictive models. Thus, our research suggests enhancing the healthcare management for patients with kidney-related diseases to avoid costly complications. We provide helpful information for medical management, which may decrease health insurance burdens in the future. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

11 pages, 692 KiB  
Article
Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients
by Yen-Chun Huang, Shao-Jung Li, Mingchih Chen, Tian-Shyug Lee and Yu-Ning Chien
Healthcare 2021, 9(5), 547; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9050547 - 07 May 2021
Cited by 18 | Viewed by 2809
Abstract
Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors [...] Read more.
Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults’ survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study’s advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients’ survival risk before a CABG operation, early prevention and disease management would be possible. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

Review

Jump to: Research, Other

21 pages, 1282 KiB  
Review
mHealth Apps for Self-Management of Cardiovascular Diseases: A Scoping Review
by Nancy Aracely Cruz-Ramos, Giner Alor-Hernández, Luis Omar Colombo-Mendoza, José Luis Sánchez-Cervantes, Lisbeth Rodríguez-Mazahua and Luis Rolando Guarneros-Nolasco
Healthcare 2022, 10(2), 322; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare10020322 - 08 Feb 2022
Cited by 20 | Viewed by 5438
Abstract
The use of mHealth apps for the self-management of cardiovascular diseases (CVDs) is an increasing trend in patient-centered care. In this research, we conduct a scoping review of mHealth apps for CVD self-management within the period 2014 to 2021. Our review revolves around [...] Read more.
The use of mHealth apps for the self-management of cardiovascular diseases (CVDs) is an increasing trend in patient-centered care. In this research, we conduct a scoping review of mHealth apps for CVD self-management within the period 2014 to 2021. Our review revolves around six main aspects of the current status of mHealth apps for CVD self-management: main CVDs managed, main app functionalities, disease stages managed, common approaches used for data extraction, analysis, management, common wearables used for CVD detection, monitoring and/or identification, and major challenges to overcome and future work remarks. Our review is based on Arksey and O’Malley’s methodological framework for conducting studies. Similarly, we adopted the PRISMA model for reporting systematic reviews and meta-analyses. Of the 442 works initially retrieved, the review comprised 38 primary studies. According to our results, the most common CVDs include arrhythmia (34%), heart failure (32%), and coronary heart disease (18%). Additionally, we found that the majority mHealth apps for CVD self-management can provide medical recommendations, medical appointments, reminders, and notifications for CVD monitoring. Main challenges in the use of mHealth apps for CVD self-management include overcoming patient reluctance to use the technology and achieving the interoperability of mHealth applications with other systems. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Graphical abstract

22 pages, 8419 KiB  
Review
Detecting Depression Signs on Social Media: A Systematic Literature Review
by Rafael Salas-Zárate, Giner Alor-Hernández, María del Pilar Salas-Zárate, Mario Andrés Paredes-Valverde, Maritza Bustos-López and José Luis Sánchez-Cervantes
Healthcare 2022, 10(2), 291; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare10020291 - 01 Feb 2022
Cited by 31 | Viewed by 7401
Abstract
Among mental health diseases, depression is one of the most severe, as it often leads to suicide; due to this, it is important to identify and summarize existing evidence concerning depression sign detection research on social media using the data provided by users. [...] Read more.
Among mental health diseases, depression is one of the most severe, as it often leads to suicide; due to this, it is important to identify and summarize existing evidence concerning depression sign detection research on social media using the data provided by users. This review examines aspects of primary studies exploring depression detection from social media submissions (from 2016 to mid-2021). The search for primary studies was conducted in five digital libraries: ACM Digital Library, IEEE Xplore Digital Library, SpringerLink, Science Direct, and PubMed, as well as on the search engine Google Scholar to broaden the results. Extracting and synthesizing the data from each paper was the main activity of this work. Thirty-four primary studies were analyzed and evaluated. Twitter was the most studied social media for depression sign detection. Word embedding was the most prominent linguistic feature extraction method. Support vector machine (SVM) was the most used machine-learning algorithm. Similarly, the most popular computing tool was from Python libraries. Finally, cross-validation (CV) was the most common statistical analysis method used to evaluate the results obtained. Using social media along with computing tools and classification methods contributes to current efforts in public healthcare to detect signs of depression from sources close to patients. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

31 pages, 2882 KiB  
Review
Current and Future Applications of Artificial Intelligence in Coronary Artery Disease
by Nitesh Gautam, Prachi Saluja, Abdallah Malkawi, Mark G. Rabbat, Mouaz H. Al-Mallah, Gianluca Pontone, Yiye Zhang, Benjamin C. Lee and Subhi J. Al’Aref
Healthcare 2022, 10(2), 232; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare10020232 - 26 Jan 2022
Cited by 16 | Viewed by 6668
Abstract
Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States [...] Read more.
Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

37 pages, 7316 KiB  
Review
A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment
by Ghalib Ahmed Tahir and Chu Kiong Loo
Healthcare 2021, 9(12), 1676; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9121676 - 03 Dec 2021
Cited by 30 | Viewed by 5678
Abstract
Dietary studies showed that dietary problems such as obesity are associated with other chronic diseases, including hypertension, irregular blood sugar levels, and increased risk of heart attacks. The primary cause of these problems is poor lifestyle choices and unhealthy dietary habits, which are [...] Read more.
Dietary studies showed that dietary problems such as obesity are associated with other chronic diseases, including hypertension, irregular blood sugar levels, and increased risk of heart attacks. The primary cause of these problems is poor lifestyle choices and unhealthy dietary habits, which are manageable using interactive mHealth apps. However, traditional dietary monitoring systems using manual food logging suffer from imprecision, underreporting, time consumption, and low adherence. Recent dietary monitoring systems tackle these challenges by automatic assessment of dietary intake through machine learning methods. This survey discusses the best-performing methodologies that have been developed so far for automatic food recognition and volume estimation. Firstly, the paper presented the rationale of visual-based methods for food recognition. Then, the core of the study is the presentation, discussion, and evaluation of these methods based on popular food image databases. In this context, this study discusses the mobile applications that are implementing these methods for automatic food logging. Our findings indicate that around 66.7% of surveyed studies use visual features from deep neural networks for food recognition. Similarly, all surveyed studies employed a variant of convolutional neural networks (CNN) for ingredient recognition due to recent research interest. Finally, this survey ends with a discussion of potential applications of food image analysis, existing research gaps, and open issues of this research area. Learning from unlabeled image datasets in an unsupervised manner, catastrophic forgetting during continual learning, and improving model transparency using explainable AI are potential areas of interest for future studies. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

18 pages, 1087 KiB  
Review
Does Artificial Intelligence Make Clinical Decision Better? A Review of Artificial Intelligence and Machine Learning in Acute Kidney Injury Prediction
by Tao Han Lee, Jia-Jin Chen, Chi-Tung Cheng and Chih-Hsiang Chang
Healthcare 2021, 9(12), 1662; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9121662 - 30 Nov 2021
Cited by 8 | Viewed by 2900
Abstract
Acute kidney injury (AKI) is a common complication of hospitalization that greatly and negatively affects the short-term and long-term outcomes of patients. Current guidelines use serum creatinine level and urine output rate for defining AKI and as the staging criteria of AKI. However, [...] Read more.
Acute kidney injury (AKI) is a common complication of hospitalization that greatly and negatively affects the short-term and long-term outcomes of patients. Current guidelines use serum creatinine level and urine output rate for defining AKI and as the staging criteria of AKI. However, because they are not sensitive or specific markers of AKI, clinicians find it difficult to predict the occurrence of AKI and prescribe timely treatment. Advances in computing technology have led to the recent use of machine learning and artificial intelligence in AKI prediction, recent research reported that by using electronic health records (EHR) the AKI prediction via machine-learning models can reach AUROC over 0.80, in some studies even reach 0.93. Our review begins with the background and history of the definition of AKI, and the evolution of AKI risk factors and prediction models is also appraised. Then, we summarize the current evidence regarding the application of e-alert systems and machine-learning models in AKI prediction. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

13 pages, 453 KiB  
Review
Sensor-Based Fall Risk Assessment: A Survey
by Guangyang Zhao, Liming Chen and Huansheng Ning
Healthcare 2021, 9(11), 1448; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9111448 - 27 Oct 2021
Cited by 10 | Viewed by 2470
Abstract
Fall is a major problem leading to serious injuries in geriatric populations. Sensor-based fall risk assessment is one of the emerging technologies to identify people with high fall risk by sensors, so as to implement fall prevention measures. Research on this domain has [...] Read more.
Fall is a major problem leading to serious injuries in geriatric populations. Sensor-based fall risk assessment is one of the emerging technologies to identify people with high fall risk by sensors, so as to implement fall prevention measures. Research on this domain has recently made great progress, attracting the growing attention of researchers from medicine and engineering. However, there is a lack of studies on this topic which elaborate the state of the art. This paper presents a comprehensive survey to discuss the development and current status of various aspects of sensor-based fall risk assessment. Firstly, we present the principles of fall risk assessment. Secondly, we show knowledge of fall risk monitoring techniques, including wearable sensor based and non-wearable sensor based. After that we discuss features which are extracted from sensors in fall risk assessment. Then we review the major methods of fall risk modeling and assessment. We also discuss some challenges and promising directions in this field at last. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

20 pages, 1681 KiB  
Review
Overview of Multi-Modal Brain Tumor MR Image Segmentation
by Wenyin Zhang, Yong Wu, Bo Yang, Shunbo Hu, Liang Wu and Sahraoui Dhelim
Healthcare 2021, 9(8), 1051; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9081051 - 16 Aug 2021
Cited by 34 | Viewed by 4399
Abstract
The precise segmentation of brain tumor images is a vital step towards accurate diagnosis and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate brain images without tissue damage or skull artifacts, providing important discriminant information for clinicians in the study [...] Read more.
The precise segmentation of brain tumor images is a vital step towards accurate diagnosis and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate brain images without tissue damage or skull artifacts, providing important discriminant information for clinicians in the study of brain tumors and other brain diseases. In this paper, we survey the field of brain tumor MRI images segmentation. Firstly, we present the commonly used databases. Then, we summarize multi-modal brain tumor MRI image segmentation methods, which are divided into three categories: conventional segmentation methods, segmentation methods based on classical machine learning methods, and segmentation methods based on deep learning methods. The principles, structures, advantages and disadvantages of typical algorithms in each method are summarized. Finally, we analyze the challenges, and suggest a prospect for future development trends. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

19 pages, 529 KiB  
Review
A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems
by Anita Ramachandran and Anupama Karuppiah
Healthcare 2021, 9(7), 914; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9070914 - 20 Jul 2021
Cited by 41 | Viewed by 5599
Abstract
Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, [...] Read more.
Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier, more affordable, and accessible. We present the relevance of machine learning in sleep apnea detection, and a study of the recent advances in the aforementioned area. The review covers research based on machine learning, deep learning, and sensor fusion, and focuses on the following facets of sleep apnea detection: (i) type of sensors used for data collection, (ii) feature engineering approaches applied on the data (iii) classifiers used for sleep apnea detection/classification. We also analyze the challenges in the design of sleep apnea detection systems, based on the literature survey. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

Other

Jump to: Research, Review

20 pages, 2301 KiB  
Systematic Review
The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review
by Mahmood Saleh Alzubaidi, Uzair Shah, Haider Dhia Zubaydi, Khalid Dolaat, Alaa A. Abd-Alrazaq, Arfan Ahmed and Mowafa Househ
Healthcare 2021, 9(6), 740; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9060740 - 16 Jun 2021
Cited by 30 | Viewed by 4394
Abstract
Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that has been ranked second after Alzheimer’s disease worldwide. Early diagnosis of PD is crucial to combat against PD to allow patients to deal with it properly. However, there is no medical test(s) available [...] Read more.
Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that has been ranked second after Alzheimer’s disease worldwide. Early diagnosis of PD is crucial to combat against PD to allow patients to deal with it properly. However, there is no medical test(s) available to diagnose PD conclusively. Therefore, computer-aided diagnosis (CAD) systems offered a better solution to make the necessary data-driven decisions and assist the physician. Numerous studies were conducted to propose CAD to diagnose PD in the early stages. No comprehensive reviews have been conducted to summarize the role of AI tools to combat PD. Objective: The study aimed to explore and summarize the applications of neural networks to diagnose PD. Methods: PRISMA Extension for Scoping Reviews (PRISMA-ScR) was followed to conduct this scoping review. To identify the relevant studies, both medical databases (e.g., PubMed) and technical databases (IEEE) were searched. Three reviewers carried out the study selection and extracted the data from the included studies independently. Then, the narrative approach was adopted to synthesis the extracted data. Results: Out of 1061 studies, 91 studies satisfied the eligibility criteria in this review. About half of the included studies have implemented artificial neural networks to diagnose PD. Numerous studies included focused on the freezing of gait (FoG). Biomedical voice and signal datasets were the most commonly used data types to develop and validate these models. However, MRI- and CT-scan images were also utilized in the included studies. Conclusion: Neural networks play an integral and substantial role in combating PD. Many possible applications of neural networks were identified in this review, however, most of them are limited up to research purposes. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-30
Back to TopTop