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DOI: 10.1055/s-0042-1742526
Novelty in Public Health and Epidemiology Informatics
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Summary
Objectives: To highlight novelty studies and current trends in Public Health and Epidemiology Informatics (PHEI).
Methods: Similar to last year’s edition, a PubMed search of 2021 scientific publications on PHEI has been conducted. The resulting references were reviewed by the two section editors. Then, 11 candidate best papers were selected from the initial 782 references. These papers were then peer-reviewed by selected external reviewers. They included at least two senior researchers, to allow the Editorial Committee of the 2022 IMIA Yearbook edition to make an informed decision for selecting the best papers of the PHEI section.
Results: Among the 782 references retrieved from PubMed, two were selected as the best papers. The first best paper reports a study which performed a comprehensive comparison of traditional statistical approaches (e.g., Cox Proportional Hazards models) vs. machine learning techniques in a large, real-world dataset for predicting breast cancer survival, with a focus on explainability. The second paper describes the engineering of deep learning models to establish associations between ocular features and major hepatobiliary diseases and to advance automated screening and identification of hepatobiliary diseases from ocular images.
Conclusion: Overall, from this year edition, we observed that the number of studies related to PHEI has decreased. The findings of the two studies selected as best papers on the topic suggest that a significant effort is still being made by the community to compare traditional learning methods with deep learning methods. Using multimodality datasets (images, texts) could improve approaches for tackling public health issues
Section Editors for the IMIA Yearbook Section on Public Health and Epidemiology Informatics
Publication History
Article published online:
04 December 2022
© 2022. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Georg Thieme Verlag KG
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References
- 1 Arora J, Mega JL, Abernethy Stadtlander A. Connecting Real-World Data to Support Public Health Efforts. NEJM Catalyst Innovations in Care Delivery; May 2022.
- 2 National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Sciences Policy; Forum on Drug Discovery, Development, and Translation. Real-World Evidence Generation and Evaluation of Therapeutics: Proceedings of a Workshop. Washington (DC): National Academies Press (US); 2017 Feb 15. 2, Improving Evidence Generation for Decision Making on Approval and Use of New Treatments: Some Stakeholder Priorities.
- 3 Diallo G, Bordea G. Public Health and Epidemiology Informatics: Recent Research Trends. Yearb Med Inform 2021 Aug;30(1):280-2.
- 4 Lamy JB, Séroussi B, Griffon N, Kerdelhué G, Jaulent MC, Bouaud J. Toward a formalization of the process to select IMIA Yearbook best papers. Methods Inf Med 2015;54(2):135-44.
- 5 Wang Z, Yin Z, Argyris YA. Detecting Medical Misinformation on Social Media Using Multimodal Deep Learning. IEEE J Biomed Health Inform 2021 Jun;25(6):2193-203.
- 6 Moncada-Torres A, van Maaren MC, Hendriks MP, Siesling S, Geleijnse G. Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival. Sci Rep 2021 Mar 26;11(1):6968.
- 7 Xiao W, Huang X, Wang JH, Lin DR, Zhu Y, Chen C, et al. Screening and identifying hepatobiliary diseases through deep learning using ocular images: a prospective, multicentre study. Lancet Digit Health 2021 Feb;3(2):e88-e97.
- 8 Herbuela VRDM, Karita T, Carvajal TM, Ho HT, Lorena JMO, et al. Early Detection of Dengue Fever Outbreaks Using a Surveillance App (Mozzify): Cross-sectional Mixed Methods Usability Study. JMIR Public Health Surveill 2021 Mar 1;7(3):e19034. Erratum in: JMIR Public Health Surveill 2021 Apr 22;7(4):e29795.
- 9 Homer J, Wakeland W. A dynamic model of the opioid drug epidemic with implications for policy. Am J Drug Alcohol Abuse 2021 Jan 2;47(1):5-15.
- 10 Kuzmenkov AY, Trushin IV, Vinogradova AG, Avramenko AA, Sukhorukova MV, Malhotra-Kumar S, et al. AMRmap: An Interactive Web Platform for Analysis of Antimicrobial Resistance Surveillance Data in Russia. Front Microbiol. 2021 Mar 12;12:620002.
- 11 Razavi-Termeh SV, Sadeghi-Niaraki A, Choi SM. Asthma-prone areas modeling using a machine learning model. Sci Rep 2021(11):1912.
- 12 Huang H, Chen Y, Ma Y. Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading. Appl Math Comput 2021 Jan 1;388:125536.
- 13 Zwan MD, van der Flier WM, Cleutjens S, Schouten TC, Vermunt L, Jutten RJ, et al. Dutch Brain Research Registry for study participant recruitment: Design and first results. Alzheimers Dement (N Y) 2021 Feb 14;7(1):e12132.
- 14 Köhler S, Gargano M, Matentzoglu N, Carmody LC, Lewis-Smith D, Vasilevsky NA, et al. The Human Phenotype Ontology in 2021. Nucleic Acids Res 2021 Jan 8;49(D1):D1207-D1217.
- 15 Vable AM, Diehl SF, Glymour MM. Code Review as a Simple Trick to Enhance Reproducibility, Accelerate Learning, and Improve the Quality of Your Team’s Research. Am J Epidemiol 2021 Oct;190(10):2172-7.