Special Issue "Simulations and Machine Learning via Big Data for Prediction, Detection, Treatment or Rehabilitation in the Healthcare Process"

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: 31 January 2022.

Special Issue Editor

Dr. Kang Hao Cheong
E-Mail Website
Chief Guest Editor
Assistant Professor, Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore 487372, Singapore
Interests: Parrondo’s paradox; evolutionary game theory; network science; data science; AI/ML in medicine/healthcare
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Special Issue Information

Dear Colleagues,

Prediction, detection, treatment, and rehabilitation are of great importance in any healthcare process. The complexity of healthcare brings with it the need to perform simulations and/or apply machine learning algorithms for prediction, detection, treatment or rehabilitation. With the rise in big data, these data can also be used to enhance the current systems of modeling or predictive analysis. Therefore, it is in the interest, as part of enhancing modern healthcare, to tap into big data using viable simulations and machine learning techniques to enhance the state of our current healthcare services.

This Special Issue offers an opportunity for novel interdisciplinary research and reviews that report on the extensive range of simulation or machine learning techniques applied to public healthcare using big data. We welcome manuscripts focusing on, but not restricted to, medical technologies, clinical practice, complexity study, simulations and validation through data fitting, machine learning, and novel methods of collecting data in healthcare. The study should cover aspects of detection, treatment or rehabilitation.

Dr. Kang Hao Cheong
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 papers will be 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 2300 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

  • early detection
  • diagnosis and treatment
  • decision making
  • classification of early symptoms
  • end of life care
  • deep learning
  • artificial intelligence and machine learning algorithms
  • big data curation
  • medical data
  • medical informatics
  • transformational healthcare
  • healthcare technology
  • assistive technology

Published Papers (5 papers)

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Research

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Article
Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data
Int. J. Environ. Res. Public Health 2021, 18(14), 7635; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18147635 - 18 Jul 2021
Viewed by 493
Abstract
Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve as screening tools for osteoporosis in adults over fifty years old. Additionally, we also compared the performance of newly developed models with [...] Read more.
Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve as screening tools for osteoporosis in adults over fifty years old. Additionally, we also compared the performance of newly developed models with traditional prediction models. Data were acquired from community-dwelling participants enrolled in health checkup programs at a medical center in Taiwan. A total of 3053 men and 2929 women were included. Models were constructed for men and women separately with artificial neural network (ANN), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression (LoR) to predict the presence of osteoporosis. Area under receiver operating characteristic curve (AUROC) was used to compare the performance of the models. We achieved AUROC of 0.837, 0.840, 0.843, 0.821, 0.827 in men, and 0.781, 0.807, 0.811, 0.767, 0.772 in women, for ANN, SVM, RF, KNN, and LoR models, respectively. The ANN, SVM, RF, and LoR models in men, and the ANN, SVM, and RF models in women performed significantly better than the traditional Osteoporosis Self-Assessment Tool for Asians (OSTA) model. We have demonstrated that machine learning algorithms improve the performance of screening for osteoporosis. By incorporating the models in clinical practice, patients could potentially benefit from earlier diagnosis and treatment of osteoporosis. Full article
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Article
An Automatic Approach Designed for Inference of the Underlying Cause-of-Death of Citizens
Int. J. Environ. Res. Public Health 2021, 18(5), 2414; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18052414 - 02 Mar 2021
Viewed by 498
Abstract
It is very important to have a comprehensive understanding of the health status of a country’s population, which helps to develop corresponding public health policies. Correct inference of the underlying cause-of-death for citizens is essential to achieve a comprehensive understanding of the health [...] Read more.
It is very important to have a comprehensive understanding of the health status of a country’s population, which helps to develop corresponding public health policies. Correct inference of the underlying cause-of-death for citizens is essential to achieve a comprehensive understanding of the health status of a country’s population. Traditionally, this relies mainly on manual methods based on medical staff’s experiences, which require a lot of resources and is not very efficient. In this work, we present our efforts to construct an automatic method to perform inferences of the underlying causes-of-death for citizens. A sink algorithm is introduced, which could perform automatic inference of the underlying cause-of-death for citizens. The results show that our sink algorithm could generate a reasonable output and outperforms other stat-of-the-art algorithms. We believe it would be very useful to greatly enhance the efficiency of correct inferences of the underlying causes-of-death for citizens. Full article
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Article
Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction
Int. J. Environ. Res. Public Health 2020, 17(11), 4179; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17114179 - 11 Jun 2020
Cited by 11 | Viewed by 1284
Abstract
The accurate prediction of ambulance demand provides great value to emergency service providers and people living within a city. It supports the rational and dynamic allocation of ambulances and hospital staffing, and ensures patients have timely access to such resources. However, this task [...] Read more.
The accurate prediction of ambulance demand provides great value to emergency service providers and people living within a city. It supports the rational and dynamic allocation of ambulances and hospital staffing, and ensures patients have timely access to such resources. However, this task has been challenging due to complex multi-nature dependencies and nonlinear dynamics within ambulance demand, such as spatial characteristics involving the region of the city at which the demand is estimated, short and long-term historical demands, as well as the demographics of a region. Machine learning techniques are thus useful to quantify these characteristics of ambulance demand. However, there is generally a lack of studies that use machine learning tools for a comprehensive modeling of the important demand dependencies to predict ambulance demands. In this paper, an original and novel approach that leverages machine learning tools and extraction of features based on the multi-nature insights of ambulance demands is proposed. We experimentally evaluate the performance of next-day demand prediction across several state-of-the-art machine learning techniques and ambulance demand prediction methods, using real-world ambulatory and demographical datasets obtained from Singapore. We also provide an analysis of this ambulatory dataset and demonstrate the accuracy in modeling dependencies of different natures using various machine learning techniques. Full article
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Review

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Review
Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification
Int. J. Environ. Res. Public Health 2021, 18(11), 6099; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18116099 - 05 Jun 2021
Cited by 1 | Viewed by 717
Abstract
Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In [...] Read more.
Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia. Full article
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Review
Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review
Int. J. Environ. Res. Public Health 2021, 18(9), 4749; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18094749 - 29 Apr 2021
Viewed by 1016
Abstract
Background: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and [...] Read more.
Background: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings. Methods: We systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency, or prehospital settings. We assessed predictive modelling studies and critically appraised the methodology and key findings of all other studies. Results: Of fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Conclusions: Current AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic. Full article
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