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Editorial Board Members' Collection Series: Artificial Intelligence and Mathematical Modeling...
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Editorial Board Members' Collection Series: Artificial Intelligence and Mathematical Modeling for Public Health—an Agenda for the Future

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 3082

Special Issue Editors

Prof. Dr. Wade Trappe

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Rutgers University, The State University of New Jersey, Piscataway, NJ 08854, USA
Interests: multimedia security; wireless security; wireless networking; cryptography
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Eva K. Lee

E-Mail Website
Guest Editor
Center for Operations Research in Medicine and Health Care, The Data and Analytics Innovation Institute, Atlanta, GA 30309, USA
Interests: mathematical theory and modeling; high-performance computing; large-scale optimization; biomedicine and clinical research; healthcare policy, management, and decision analysis; public health and medical preparedness; homeland security and defense; industrial applications
Dr. Oliver Faust

E-Mail Website
Guest Editor
College of Business, Technology & Engineering, Sheffield Hallam University, Sheffield S1 1WB, UK
Interests: artificial intelligence; formal methods; biomedical signal processing; service based healthcare; intelligent internet of medical things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce this collection titled “Editorial Board Members' Collection Series: Artificial Intelligence and Mathematical Modeling in the Public Health Agenda”. This issue will be a collection of papers, and researchers invited by the Editorial Board Members. The aim is to provide a venue for networking and communication between IJERPH and scholars in the field of AI, biology, mathematical modeling and health. All papers will be published with fully open access after peer review.

Prof. Dr. Wade Trappe
Prof. Dr. Eva K. Lee
Dr. Oliver Faust
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. 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

  • artificial intelligence
  • deep learning for medical diagnosis
  • epidemiological modeling
  • biomedical healthcare
  • digital health
  • public health
  • disaster medicine

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

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Research

12 pages, 1193 KiB  
Article
Automated Detection of Patients at High Risk of Polypharmacy including Anticholinergic and Sedative Medications
by Amirali Shirazibeheshti, Alireza Ettefaghian, Farbod Khanizadeh, George Wilson, Tarek Radwan and Cristina Luca
Int. J. Environ. Res. Public Health 2023, 20(12), 6178; https://doi.org/10.3390/ijerph20126178 - 19 Jun 2023
Cited by 2 | Viewed by 2551
Abstract
Ensuring that medicines are prescribed safely is fundamental to the role of healthcare professionals who need to be vigilant about the risks associated with drugs and their interactions with other medicines (polypharmacy). One aspect of preventative healthcare is to use artificial intelligence to [...] Read more.
Ensuring that medicines are prescribed safely is fundamental to the role of healthcare professionals who need to be vigilant about the risks associated with drugs and their interactions with other medicines (polypharmacy). One aspect of preventative healthcare is to use artificial intelligence to identify patients at risk using big data analytics. This will improve patient outcomes by enabling pre-emptive changes to medication on the identified cohort before symptoms present. This paper presents a mean-shift clustering technique used to identify groups of patients at the highest risk of polypharmacy. A weighted anticholinergic risk score and a weighted drug interaction risk score were calculated for each of 300,000 patient records registered with a major regional UK-based healthcare provider. The two measures were input into the mean-shift clustering algorithm and this grouped patients into clusters reflecting different levels of polypharmaceutical risk. Firstly, the results showed that, for most of the data, the average scores are not correlated and, secondly, the high risk outliers have high scores for one measure but not for both. These suggest that any systematic recognition of high-risk groups should consider both anticholinergic and drug–drug interaction risks to avoid missing high-risk patients. The technique was implemented in a healthcare management system and easily and automatically identifies groups at risk far faster than the manual inspection of patient records. This is much less labour-intensive for healthcare professionals who can focus their assessment only on patients within the high-risk group(s), enabling more timely clinical interventions where necessary. Full article
(This article belongs to the Special Issue Editorial Board Members' Collection Series: Artificial Intelligence and Mathematical Modeling for Public Health—an Agenda for the Future)
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