Submit to Healthcare Review for Healthcare Propose a Special Issue

Journal Browser

Feature Papers in Artificial Intelligence in Medicine

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Published Papers

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 2020) | Viewed by 79227

Share This Special Issue

Special Issue Editors

Prof. Dr. Tin-Chih Toly Chen

E-Mail Website
Guest Editor

Department of Industrial Engineering and Management, National Chiao Tung University, 1001 University Road, Hsinchu 30010, Taiwan
Interests: ambient intelligence; ergonomics; cloud and virtual manufacturing; applied soft computing
Special Issues, Collections and Topics in MDPI journals

Dr. Michael O'Grady

E-Mail Website
Guest Editor

School of Computer Science, University College Dublin, Dublin, Ireland
Interests: ambient intelligence; pervasive computing; ubiquitous sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence comprises applications of computer and information technologies to simulate human and biological intelligence or natural phenomena so as to assist human beings in exploring and monitoring environments, making decisions, identifying patterns, classifying objects, and so on. Recently, artificial intelligence applications have attracted a great deal of attention due to the rapid advances in computer, communication, and sensing technologies. Such advances are especially advantageous for solving problems in medical fields that involve big data, information heterogeneity, complicated cause-and-effect relationships, and other challenges. So far, the applications of artificial intelligence in medicine have considerably improved the quality, user satisfaction, cost-effectiveness, and efficiency of medical and healthcare services.

This Special Issue aims to provide technical details of artificial intelligence in medicine. These details will hold great interest for researchers in artificial intelligence, medicine, healthcare, smart technology, quality technology, quantitative management, ambient intelligence, mobile commerce, operations research, system science, and information management, as well as for practicing managers and engineers. This Special Issue will feature a balance between state-of-the-art research and practical applications. This Special Issue also provides a forum for researchers and practitioners to review and disseminate quality research work on artificial intelligence applications to medical and healthcare and the critical issues for further development.

Topics of interest include, but are not limited to:

3D printing
Ant colony optimization
Artificial bee colony
Artificial neural network
Bacterial colony foraging
Deep learning
Fuzzy logic
Grey wolf optimization
Medical robots
Natural language processing
Social intelligence
Software agents
Spider monkey optimization
Swam intelligence
Virtual and augmented reality

with applications in

Clinical decision support
Disease diagnosis, classification and prediction
Health monitoring
Healthcare service recommendation
Hospital scheduling
Medical data analysis
Medical image analysis
Medical innovations
Smart hospital
Telehealth, telemedicine

Prof. Dr. Tin-Chih Chen
Dr. Michael O'Grady
Guest Editors

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.

Published Papers (12 papers)

Download All Papers

Research

14 pages, 9493 KiB

Open AccessArticle

A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network

by Francisco Javier Díaz-Pernas, Mario Martínez-Zarzuela, Míriam Antón-Rodríguez and David González-Ortega

Healthcare 2021, 9(2), 153; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9020153 - 02 Feb 2021

Cited by 233 | Viewed by 10760

Abstract

In this paper, we present a fully automatic brain tumor segmentation and classification model using a Deep Convolutional Neural Network that includes a multiscale approach. One of the differences of our proposal with respect to previous works is that input images are processed in three spatial scales along different processing pathways. This mechanism is inspired in the inherent operation of the Human Visual System. The proposed neural model can analyze MRI images containing three types of tumors: meningioma, glioma, and pituitary tumor, over sagittal, coronal, and axial views and does not need preprocessing of input images to remove skull or vertebral column parts in advance. The performance of our method on a publicly available MRI image dataset of 3064 slices from 233 patients is compared with previously classical machine learning and deep learning published methods. In the comparison, our method remarkably obtained a tumor classification accuracy of 0.973, higher than the other approaches using the same database. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence in Medicine)

► Show Figures

Figure 1

19 pages, 5768 KiB

Open AccessArticle

Solving Operating Room Scheduling Problem Using Artificial Bee Colony Algorithm

by Yang-Kuei Lin and Min-Yang Li

Healthcare 2021, 9(2), 152; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9020152 - 02 Feb 2021

Cited by 11 | Viewed by 3287

Abstract

Many healthcare institutions are interested in reducing costs and in maintaining a good quality of care. The operating room department is typically one of the most costly units in a hospital. Hospital managers are always interested in finding effective ways of using operating rooms to minimize operating costs. In this research, we study the operating room scheduling problem. We consider the use of a weekly surgery schedule with an open scheduling strategy that takes into account the availabilities of surgeons and operating rooms. The objective is to minimize the total operating cost while maximizing the utilization of the operating rooms but also minimizing overtime use. A revised mathematical model is proposed that can provide optimal solutions for a surgery size up to 110 surgical cases. Next, two modified heuristics, based on the earliest due date (EDD) and longest processing time (LPT) rules, are proposed to quickly find feasible solutions to the studied problem. Finally, an artificial bee colony (ABC) algorithm that incorporates the initial solutions, a recovery scheme, local search schemes, and an elitism strategy is proposed. The computational results show that, for a surgery size between 40 and 100 surgical cases, the ABC algorithm found optimal solutions to all of the tested problems. For surgery sizes larger than 110 surgical cases, the ABC algorithm performed significantly better than the two proposed heuristics. The computational results indicate that the proposed ABC is promising and capable of solving large problems. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence in Medicine)

► Show Figures

Figure 1

14 pages, 3034 KiB

Open AccessArticle

Analysis of Correlation between Climate Change and Human Health Based on a Machine Learning Approach

by Vito Alberto Pizzulli, Vito Telesca and Gabriela Covatariu

Healthcare 2021, 9(1), 86; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9010086 - 17 Jan 2021

Cited by 5 | Viewed by 3764

Abstract

Climate change increasingly affects every aspect of human life. Recent studies report a close correlation with human health and it is estimated that global death rates will increase by 73 per 100,000 by 2100 due to changes in temperature. In this context, the present work aims to study the correlation between climate change and human health, on a global scale, using artificial intelligence techniques. Starting from previous studies on a smaller scale, that represent climate change and which at the same time can be linked to human health, four factors were chosen. Four causes of mortality, strongly correlated with the environment and climatic variability, were subsequently selected. Various analyses were carried out, using neural networks and machine learning to find a correlation between mortality due to certain diseases and the leading causes of climate change. Our findings suggest that anthropogenic climate change is strongly correlated with human health; some diseases are mainly related to risk factors while others require a more significant number of variables to derive a correlation. In addition, a forecast of victims related to climate change was formulated. The predicted scenario confirms that a prevalently increasing trend in climate change factors corresponds to an increase in victims. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence in Medicine)

► Show Figures

Figure 1

23 pages, 3301 KiB

Open AccessArticle

Analyzing the Impact of Vaccine Availability on Alternative Supplier Selection Amid the COVID-19 Pandemic: A cFGM-FTOPSIS-FWI Approach

by Toly Chen, Yu-Cheng Wang and Hsin-Chieh Wu

Healthcare 2021, 9(1), 71; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9010071 - 13 Jan 2021

Cited by 32 | Viewed by 3145

Abstract

The supply chain disruption caused by the coronavirus disease 2019 (COVID-19) pandemic has forced many manufacturers to look for alternative suppliers. How to choose a suitable alternative supplier in the COVID-19 pandemic has become an important task. To fulfill this task, this research proposes a calibrated fuzzy geometric mean (cFGM)-fuzzy technique for order preference by similarity to ideal solution (FTOPSIS)-fuzzy weighted intersection (FWI) approach. In the proposed methodology, first, the cFGM method is proposed to accurately derive the priorities of criteria. Subsequently, each expert applies the FTOPSIS method to compare the overall performances of alternative suppliers in the COVID-19 pandemic. The sensitivity of an expert to any change in the overall performance of the alternative supplier is also considered. Finally, the FWI operator is used to aggregate the comparison results by all experts, for which an expert’s authority level is set to a value proportional to the consistency of his/her pairwise comparison results. The cFGM-FTOPSIS-FWI approach has been applied to select suitable alternative suppliers for a Taiwanese foundry in the COVID-19 pandemic. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence in Medicine)

► Show Figures

Figure 1

13 pages, 1786 KiB

Open AccessArticle

A Lightweight Convolutional Neural Network Architecture Applied for Bone Metastasis Classification in Nuclear Medicine: A Case Study on Prostate Cancer Patients

by Charis Ntakolia, Dimitrios E. Diamantis, Nikolaos Papandrianos, Serafeim Moustakidis and Elpiniki I. Papageorgiou

Healthcare 2020, 8(4), 493; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare8040493 - 18 Nov 2020

Cited by 24 | Viewed by 3587

Abstract

Bone metastasis is among the most frequent in diseases to patients suffering from metastatic cancer, such as breast or prostate cancer. A popular diagnostic method is bone scintigraphy where the whole body of the patient is scanned. However, hot spots that are presented in the scanned image can be misleading, making the accurate and reliable diagnosis of bone metastasis a challenge. Artificial intelligence can play a crucial role as a decision support tool to alleviate the burden of generating manual annotations on images and therefore prevent oversights by medical experts. So far, several state-of-the-art convolutional neural networks (CNN) have been employed to address bone metastasis diagnosis as a binary or multiclass classification problem achieving adequate accuracy (higher than 90%). However, due to their increased complexity (number of layers and free parameters), these networks are severely dependent on the number of available training images that are typically limited within the medical domain. Our study was dedicated to the use of a new deep learning architecture that overcomes the computational burden by using a convolutional neural network with a significantly lower number of floating-point operations (FLOPs) and free parameters. The proposed lightweight look-behind fully convolutional neural network was implemented and compared with several well-known powerful CNNs, such as ResNet50, VGG16, Inception V3, Xception, and MobileNet on an imaging dataset of moderate size (778 images from male subjects with prostate cancer). The results prove the superiority of the proposed methodology over the current state-of-the-art on identifying bone metastasis. The proposed methodology demonstrates a unique potential to revolutionize image-based diagnostics enabling new possibilities for enhanced cancer metastasis monitoring and treatment. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence in Medicine)

► Show Figures

Figure 1

26 pages, 2943 KiB

Open AccessArticle

Assessing the Robustness of a Factory Amid the COVID-19 Pandemic: A Fuzzy Collaborative Intelligence Approach

by Toly Chen, Yu-Cheng Wang and Min-Chi Chiu

Healthcare 2020, 8(4), 481; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare8040481 - 12 Nov 2020

Cited by 17 | Viewed by 2736

Abstract

The COVID-19 pandemic has affected the operations of factories worldwide. However, the impact of the COVID-19 pandemic on different factories is not the same. In other words, the robustness of factories to the COVID-19 pandemic varies. To explore this topic, this study proposes a fuzzy collaborative intelligence approach to assess the robustness of a factory to the COVID-19 pandemic. In the proposed methodology, first, a number of experts apply a fuzzy collaborative intelligence approach to jointly evaluate the relative priorities of factors that affect the robustness of a factory to the COVID-19 pandemic. Subsequently, based on the evaluated relative priorities, a fuzzy weighted average method is applied to assess the robustness of a factory to the COVID-19 pandemic. The assessment result can be compared with that of another factory using a fuzzy technique for order preference by similarity to ideal solution. The proposed methodology has been applied to assess the robustness of a wafer fabrication factory in Taiwan to the COVID-19 pandemic. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence in Medicine)

► Show Figures

Figure 1

19 pages, 436 KiB

Open AccessArticle

Automatic Incident Triage in Radiation Oncology Incident Learning System

by Khajamoinuddin Syed, William Sleeman IV, Michael Hagan, Jatinder Palta, Rishabh Kapoor and Preetam Ghosh

Healthcare 2020, 8(3), 272; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare8030272 - 14 Aug 2020

Cited by 11 | Viewed by 3466

Abstract

The Radiotherapy Incident Reporting and Analysis System (RIRAS) receives incident reports from Radiation Oncology facilities across the US Veterans Health Affairs (VHA) enterprise and Virginia Commonwealth University (VCU). In this work, we propose a computational pipeline for analysis of radiation oncology incident reports. Our pipeline uses machine learning (ML) and natural language processing (NLP) based methods to predict the severity of the incidents reported in the RIRAS platform using the textual description of the reported incidents. These incidents in RIRAS are reviewed by a radiation oncology subject matter expert (SME), who initially triages some incidents based on the salient elements in the incident report. To automate the triage process, we used the data from the VHA treatment centers and the VCU radiation oncology department. We used NLP combined with traditional ML algorithms, including support vector machine (SVM) with linear kernel, and compared it against the transfer learning approach with the universal language model fine-tuning (ULMFiT) algorithm. In RIRAS, severities are divided into four categories; A, B, C, and D, with A being the most severe to D being the least. In this work, we built models to predict High (A & B) vs. Low (C & D) severity instead of all the four categories. Models were evaluated with macro-averaged precision, recall, and F1-Score. The Traditional ML machine learning (SVM-linear) approach did well on the VHA dataset with 0.78 F1-Score but performed poorly on the VCU dataset with 0.5 F1-Score. The transfer learning approach did well on both datasets with 0.81 F1-Score on VHA dataset and 0.68 F1-Score on the VCU dataset. Overall, our methods show promise in automating the triage and severity determination process from radiotherapy incident reports. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence in Medicine)

► Show Figures

Figure 1

42 pages, 2296 KiB

Open AccessArticle

An Evaluation of Graphical Formats for the Summary of Activities of Daily Living (ADLs)

by Caroline A. Byrne, Michael O’Grady, Rem Collier and Gregory M. P. O’Hare

Healthcare 2020, 8(3), 194; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare8030194 - 01 Jul 2020

Cited by 5 | Viewed by 3162

Abstract

Activities of Daily Living systems (ADLs) and the User Interface (UI) design principles used to implement them empowers the elderly to continue living a normal daily routine. The daily monitoring of activities for most Assisted Living (AL) systems demands/necessitates accurate daily user interaction, and the design principles for these systems often focus on the UI usability for the elder, not the caregiver/family member. This paper reviews Ambient Assisted Living (AAL) and ADLs UI designs and evaluates the usability of ADLs visualisation tools for caregivers. Results indicate that the UI presenting information in a bar graph format was the preferred option for respondents, as 60% chose this summarisation method over the alternative line graph UI, which had 38% of respondents selecting this format for information representation. Therefore, when designing Ambient Assisted Living (AAL) UIs, it is recommended that short periods of time are best presented in a pie graph format in combination with a bar graph format for representing extended timeline information to caregivers about their loved ones. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence in Medicine)

► Show Figures

Figure 1

13 pages, 5198 KiB

Open AccessArticle

Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

by Patricia Melin, Julio Cesar Monica, Daniela Sanchez and Oscar Castillo

Healthcare 2020, 8(2), 181; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare8020181 - 19 Jun 2020

Cited by 138 | Viewed by 7559

Abstract

In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence in Medicine)

► Show Figures

Figure 1

8 pages, 920 KiB

Open AccessArticle

AI Chatbot Design during an Epidemic like the Novel Coronavirus

by Gopi Battineni, Nalini Chintalapudi and Francesco Amenta

Healthcare 2020, 8(2), 154; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare8020154 - 03 Jun 2020

Cited by 90 | Viewed by 27637

Abstract

Since the discovery of the Coronavirus (nCOV-19), it has become a global pandemic. At the same time, it has been a great challenge to hospitals or healthcare staff to manage the flow of the high number of cases. Especially in remote areas, it is becoming more difficult to consult a medical specialist when the immediate hit of the epidemic has occurred. Thus, it becomes obvious that if effectively designed and deployed chatbot can help patients living in remote areas by promoting preventive measures, virus updates, and reducing psychological damage caused by isolation and fear. This study presents the design of a sophisticated artificial intelligence (AI) chatbot for the purpose of diagnostic evaluation and recommending immediate measures when patients are exposed to nCOV-19. In addition, presenting a virtual assistant can also measure the infection severity and connects with registered doctors when symptoms become serious. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence in Medicine)

► Show Figures

Figure 1

17 pages, 3578 KiB

Open AccessArticle

Constructing Constraint-Based Simulation System for Creating Emergency Evacuation Plans: A Case of an Outpatient Chemotherapy Area at a Cancer Medical Center

by I-Chen Wu, Yi-Chun Lin, Huey-Wen Yien and Fuh-Yuan Shih

Healthcare 2020, 8(2), 137; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare8020137 - 20 May 2020

Cited by 11 | Viewed by 4567

Abstract

Making emergency evacuation plans for disaster prevention is always a high priority for hospital administrators to ensure the safety of patients and employees. This study employs the outpatient chemotherapy area of a cancer medical center as an example, and its area involves professional medical care and relatively complex human group behaviors. Hence, it is necessary to simulate evacuations in advance to formulate a special evacuation plan. To achieve this task, a constraint-based simulation system is developed with three major processes: defining spatial and activity constraints, agent-based modeling, and optimizing resource allocation. The spatial boundaries are converted from a three-dimensional model in the Building Information Modeling (BIM) to conduct a visualized simulation. Based on the spatial boundaries, the activities of the agents are set to obey the process specified by work studies. Finally, the Monte Carlo method is employed to simulate the stochastic rescue behaviors of nurses during disasters to determine the fittest resource allocation with the shortest evacuation time for different numbers of patients. The results reveal that the proposed system can output a suggested list of resource allocations and visualized results for administrators when making evacuation plans such that all the people in the area can be safely evacuated. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence in Medicine)

► Show Figures

Figure 1

21 pages, 5272 KiB

Open AccessArticle

Analyzing Lung Disease Using Highly Effective Deep Learning Techniques

by Krit Sriporn, Cheng-Fa Tsai, Chia-En Tsai and Paohsi Wang

Healthcare 2020, 8(2), 107; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare8020107 - 23 Apr 2020

Cited by 22 | Viewed by 4344

Abstract

Image processing technologies and computer-aided diagnosis are medical technologies used to support decision-making processes of radiologists and medical professionals who provide treatment for lung disease. These methods involve using chest X-ray images to diagnose and detect lung lesions, but sometimes there are abnormal cases that take some time to occur. This experiment used 5810 images for training and validation with the MobileNet, Densenet-121 and Resnet-50 models, which are popular networks used to classify the accuracy of images, and utilized a rotational technique to adjust the lung disease dataset to support learning with these convolutional neural network models. The results of the convolutional neural network model evaluation showed that Densenet-121, with a state-of-the-art Mish activation function and Nadam-optimized performance. All the rates for accuracy, recall, precision and F1 measures totaled 98.88%. We then used this model to test 10% of the total images from the non-dataset training and validation. The accuracy rate was 98.97% for the result which provided significant components for the development of a computer-aided diagnosis system to yield the best performance for the detection of lung lesions. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence in Medicine)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Feature Papers in Artificial Intelligence in Medicine

Share This Special Issue

Special Issue Editors

Special Issue Information

Published Papers (12 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI