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Machine Learning for Healthcare Applications
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Machine Learning for Healthcare Applications

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Care Sciences & Services".

Deadline for manuscript submissions: closed (30 March 2023) | Viewed by 6668

Special Issue Editor

Dr. Kenneth Lee Sutherland

E-Mail Website
Guest Editor
Faculty of Medicine, Hokkaido University, Hokkaido 060-0808, Japan
Interests: computer programming; medical physics; AI; windows user interface

Special Issue Information

Dear Colleagues,

When I was a student studying computer science in the 1980s, I took a course in artificial intelligence. I was deeply disappointed. In those days, AI involved building a list of rules. For example, if a patient presents with a cough, high fever, and trouble smelling, then he or she probably has COVID-19. Duh. Plus, we had to program in LISP, which eschewed iteration in favor of recursion. Very strange. They said that AI was going to become mainstream in five years. It was always five years in the future. However, now, some forty years on, with the recent development of neural networks programmed with Python, AI is finally showing its potential. Instead of a list of rules, the network is trained on previously obtained data. The data can be in the form of images or test results. For example, a neural network can be trained to detect likely COVID-19 cases by feeding it a list of labeled normal and positive case data. The trained network can then be used to detect cases that might otherwise be overlooked by humans. Beyond diagnosis, AI is showing promise in various fields of medicine, from individualized medication to hospital management; software tools based on artificial neural networks are being developed to assist doctors and staff. We hope that this Special Issue will introduce a variety of these recent developments of AI in public health.

Dr. Kenneth Lee Sutherland
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. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly 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 2500 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

  • automated disease diagnosis
  • telemedicine
  • medical imaging
  • smart health monitoring
  • social media healthcare
  • machine learning for COVID-19

Published Papers (3 papers)

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Research

17 pages, 5477 KiB  
Article
A Machine Learning Approach for Monitoring and Classifying Healthcare Data—A Case of Emergency Department of KSA Hospitals
by Mahmoud Ragab, Faris Kateb, Mohammed W. Al-Rabia, Diaa Hamed, Turki Althaqafi and Abdullah S. AL-Malaise AL-Ghamdi
Int. J. Environ. Res. Public Health 2023, 20(6), 4794; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph20064794 - 8 Mar 2023
Cited by 2 | Viewed by 1811
Abstract
The Emergency Departments (EDs), in hospitals located in a few important areas in Saudi Arabia, experience a heavy inflow of patients due to viral illnesses, pandemics, and even on a few special occasions events such as Hajj or Umrah, when pilgrims travel from [...] Read more.
The Emergency Departments (EDs), in hospitals located in a few important areas in Saudi Arabia, experience a heavy inflow of patients due to viral illnesses, pandemics, and even on a few special occasions events such as Hajj or Umrah, when pilgrims travel from one region to another with severe disease conditions. Apart from the EDs, it is critical to monitor the movements of patients from EDs to other wards inside the hospital or in the region. This is to track the spread of viral illnesses that require more attention. In this scenario, Machine Learning (ML) algorithms can be used to classify the data into many classes and track the target audience. The current research article presents a Machine Learning-based Medical Data Monitoring and Classification Model for the EDs of the KSA hospitals and is named MLMDMC-ED technique. The most important aim of the proposed MLMDMC-ED technique is to monitor and track the patient’s visits to the EDs, the treatment given to them based on the Canadian Emergency Department Triage and Acuity Scale (CTAS), and their Length Of Stay (LOS) in the hospital, based on their treatment requirements. A patient’s clinical history is crucial in terms of making decisions during health emergencies or pandemics. So, the data should be processed so that it can be classified and visualized in different formats using the ML technique. The current research work aims at extracting the textual features from the patients’ data using the metaheuristic Non-Defeatable Genetic Algorithm II (NSGA II). The data, collected from the hospitals, are classified using the Graph Convolutional Network (GCN) model. Grey Wolf Optimizer (GWO) is exploited for fine-tuning the parameters to optimize the performance of the GCN model. The proposed MLMDMC-ED technique was experimentally validated on the healthcare data and the outcomes indicated the improvements of the MLMDMC-ED technique over other models with a maximum accuracy of 91.87%. Full article
(This article belongs to the Special Issue Machine Learning for Healthcare Applications)
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21 pages, 5352 KiB  
Article
Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network
by Madhusmita Das, Rasmita Dash and Sambit Kumar Mishra
Int. J. Environ. Res. Public Health 2023, 20(3), 2131; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph20032131 - 24 Jan 2023
Cited by 16 | Viewed by 2393
Abstract
Worldwide, oral cancer is the sixth most common type of cancer. India is in 2nd position, with the highest number of oral cancer patients. To the population of oral cancer patients, India contributes to almost one-third of the total count. Among several types [...] Read more.
Worldwide, oral cancer is the sixth most common type of cancer. India is in 2nd position, with the highest number of oral cancer patients. To the population of oral cancer patients, India contributes to almost one-third of the total count. Among several types of oral cancer, the most common and dominant one is oral squamous cell carcinoma (OSCC). The major reason for oral cancer is tobacco consumption, excessive alcohol consumption, unhygienic mouth condition, betel quid eating, viral infection (namely human papillomavirus), etc. The early detection of oral cancer type OSCC, in its preliminary stage, gives more chances for better treatment and proper therapy. In this paper, author proposes a convolutional neural network model, for the automatic and early detection of OSCC, and for experimental purposes, histopathological oral cancer images are considered. The proposed model is compared and analyzed with state-of-the-art deep learning models like VGG16, VGG19, Alexnet, ResNet50, ResNet101, Mobile Net and Inception Net. The proposed model achieved a cross-validation accuracy of 97.82%, which indicates the suitability of the proposed approach for the automatic classification of oral cancer data. Full article
(This article belongs to the Special Issue Machine Learning for Healthcare Applications)
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15 pages, 3833 KiB  
Article
Infant Low Birth Weight Prediction Using Graph Embedding Features
by Wasif Khan, Nazar Zaki, Amir Ahmad, Jiang Bian, Luqman Ali, Mohammad Mehedy Masud, Nadirah Ghenimi and Luai A. Ahmed
Int. J. Environ. Res. Public Health 2023, 20(2), 1317; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph20021317 - 11 Jan 2023
Cited by 4 | Viewed by 1980
Abstract
Low Birth weight (LBW) infants pose a serious public health concern worldwide in both the short and long term for infants and their mothers. Infant weight prediction prior to birth can help to identify risk factors and reduce the risk of infant morbidity [...] Read more.
Low Birth weight (LBW) infants pose a serious public health concern worldwide in both the short and long term for infants and their mothers. Infant weight prediction prior to birth can help to identify risk factors and reduce the risk of infant morbidity and mortality. Although many Machine Learning (ML) algorithms have been proposed for LBW prediction using maternal features and produced considerable model performance, their performance needs to be improved so that they can be adapted in real-world clinical settings. Existing algorithms used for LBW classification often fail to capture structural information from the tabular dataset of patients with different complications. Therefore, to improve the LBW classification performance, we propose a solution by transforming the tabular data into a knowledge graph with the aim that patients from the same class (normal or LBW) exhibit similar patterns in the graphs. To achieve this, several features related to each node are extracted such as node embedding using node2vec algorithm, node degree, node similarity, nearest neighbors, etc. Our method is evaluated on a real-life dataset obtained from a large cohort study in the United Arab Emirates which contains data from 3453 patients. Multiple experiments were performed using the seven most commonly used ML models on the original dataset, graph features, and a combination of features, respectively. Experimental results show that our proposed method achieved the best performance with an area under the curve of 0.834 which is over 6% improvement compared to using the original risk factors without transforming them into knowledge graphs. Furthermore, we provide the clinical relevance of the proposed model that are important for the model to be adapted in clinical settings. Full article
(This article belongs to the Special Issue Machine Learning for Healthcare Applications)
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