Novelty in Public Health and Epidemiology Informatics

Abstract

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

Predicting over-the-counter antibiotic use in rural Pune, India, using machine learning methods

“…ML models have proven to be helpful in the medical and health sciences, particularly in the areas of diagnosis and outcome prediction (27). Previous research has suggested that the application of ML models in the healthcare industry, although still in the early stages, is primarily focused on the early diagnosis of chronic diseases, predicting future disease incidence, conducting epidemiological studies, and facilitating evidence-based decision-making (27)(28)(29)(30)(31)(32). There is also evidence supporting the use of AI and ML models to predict AMR among bacterial species based on whole genome sequencing (12,13,(34)(35)(36)(37)(38).…”

Broad Trends in Public Health and Epidemiology Informatics

“…Similarly to the last edition of the IMIA Yearbook for the PHEI section [3], a comprehensive literature search was performed by the section editors using the PubMed/ MEDLINE database from the National Center for Biotechnology Information (NCBI). A large set of MeSH descriptors were used to retrieve relevant studies ranging from January 1, 2022 to December 31, 2022.…”

Informatics for One Health

“…Among the two best papers presented by Georgeta Bordea, Gayo Diallo and Cécilia Samieri, the editors of the Public Health and Epidemiology Informatics (PHEI) section [32], the paper from Valentin et al [33] was linked to the One Health topic, more precisely to the annotation of news articles containing epidemiological information on animal diseases. Following on from the theme of the previous yearbook, He et al have carried out a review of recent works which take account of the social determinants of health to promote equity in health [34].…”