ijerph-logo

Journal Browser

Journal Browser

Special Issue "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 Communication and Informatics".

Deadline for manuscript submissions: 30 September 2023 | Viewed by 975

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 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 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 (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network
Int. J. Environ. Res. Public Health 2023, 20(3), 2131; https://doi.org/10.3390/ijerph20032131 (registering DOI) - 24 Jan 2023
Viewed by 342
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)
Show Figures

Figure 1

Article
Infant Low Birth Weight Prediction Using Graph Embedding Features
Int. J. Environ. Res. Public Health 2023, 20(2), 1317; https://doi.org/10.3390/ijerph20021317 - 11 Jan 2023
Viewed by 431
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)
Show Figures

Figure 1

Back to TopTop