Subscribe to RSS
DOI: 10.1055/s-0043-1768754
Broad Trends in Public Health and Epidemiology Informatics
Summary
Objectives: The objective of this study is to highlight innovative research and contemporary trends in the area of Public Health and Epidemiology Informatics (PHEI).
Methods: Following a similar approach to last year's edition, a meticulous search was conducted on PubMed (with keywords including topics related to Public Health, Epidemiological Surveillance and Medical Informatics), examining a total of 2,022 scientific publications on Public Health and Epidemiology Informatics (PHEI). The resulting references were thoroughly examined by the three section editors. Subsequently, 10 papers were chosen as potential candidates for the best paper award. These selected papers were then subjected to peer-review by six external reviewers, in addition to the section editors and two chief editors of the IMIA yearbook of medical informatics. Each paper underwent a total of five reviews.
Results: Out of the 539 references retrieved from PubMed, only two were deemed worthy of the best paper award, although four papers had the potential to qualify in total. The first best paper by pertains to a study about the need for a new annotation framework due to inadequacies in existing methods and resources. The second paper elucidates the use of Weibo data to monitor the health of Chinese urbanites. The correlation between air pollution and health sensing was measured via generalized additive models.
Conclusions: One of the primary findings of this edition is the dearth of studies identified for the PHEI section, which represents a significant decline compared to the previous edition. This is particularly surprising given that the post-COVID period should have led to an increased use of information and communication technology for public health issues.
Publication History
Article published online:
26 December 2023
© 2023. 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
Rüdigerstraße 14, 70469 Stuttgart, Germany
-
References
- 1 Al-Regaiey KA, Alshamry WS, Alqarni RA, Albarrak MK, Alghoraiby RM, Alkadi DY, et al. Influence of social media on parents' attitudes towards vaccine administration. Hum Vaccin Immunother 2022 ;18(1):1872340. doi: 10.1080/21645515.2021.1872340.
- 2 Brakefield WS, Ammar N, Shaban-Nejad A. An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model. JMIR Form Res 2022 ;6(7):e36055. doi: 10.2196/36055.
- 3 Diallo G, Bordea G. Novelty in Public Health and Epidemiology Informatics. Yearb Med Inform 2022 ;31(1):273–5. doi: 10.1055/s-0042-1742526.
- 4 Fu L, Xia W, Shi W, Cao G-X, Ruan Y-T, Zhao X-Y, et al. Deep learning based cervical screening by the cross-modal integration of colposcopy, cytology, and HPV test. Int J Med Inform 2022;159:104675. doi: 10.1016/j.ijmedinf.2021.104675.
- 5 Humbert-Droz M Izadi Z, Schmajuk G, Giranfrancesco M, Baker MC, Yazdany J, et al. Development of a natural language processing system for extracting rheumatoid arthritis outcomes from clinical notes using the national RISE registry. Arthritis Care Res (Hoboken) 2023;75(3):608–15. Epub 2022 Oct 31. doi: 10.1002/acr.24869.
- 6 Ji H, Wang J, Meng B, Cao Z, Yan T, Zhi G, et al. Research on adaption to air pollution in Chinese cities: Evidence from social media-based health sensing. Environ Res 2022 ;210:112762. doi: 10.1016/j.envres.2022.112762
- 7 Lamy J-B, Séroussi B, Griffon N, Kerdelhué G, Jaulent M-C, Bouaud J. Toward a formalization of the process to select IMIA Yearbook best papers. Methods Inf Med 2015 ;54(2):135–44. https://doi.org/10.3414/ME14-01-0031.
- 8 Mougin F, Hollis KF, Soualmia LF. Inclusive Digital Health. Yearb Med Inform 2022;31(1):2–6. https://doi.org/10.1055/s-0042-1742540.
- 9 Rieckmann A, Dworzynski P, Arras L, Lapuschkin S, Samek W, Aniweta Arah O, et al. Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. Int J Epidemiol 2022;51(5):1622–36. doi: 10.1093/ije/dyac078.
- 10 Su P-Y, Wei Y-C, Luo H, Liu C-H, Huan W-Y, Chen K-F, et al. Machine Learning Models for Predicting Influential Factors of Early Outcomes in Acute Ischemic Stroke: Registry-Based Study. JMIR Med Inform 2022;10(3):e32508. doi: 10.2196/32508.
- 11 Valentin S, Arsevska E, Vilain A, De Waele V, Lancelot R, Roche M. Elaboration of a new framework for fine-grained epidemiological annotation. Sci Data 2022;9(1):655. doi: 10.1038/s41597-022-01743-2
- 12 Wang S, Celebi ME, Zhang Y-D, Hu J, Dong S. Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects. Information Fusion 2021;76:376–421. doi: 10.1016/j.inffus.2021.07.001.
- 13 Yadgir SR, Engstrom CJ, Jacobsohn GC, Green RK, Jones CMC, Cushman JT, et al. Machine learning-assisted screening for cognitive impairment in the emergency department. J Am Geriatr Soc 2022;70(3):831-7. doi: 10.1111/jgs.17491.
- 14 Yu H, Huang T, Feng B, Lyu, J. Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a surveillance, epidemiology, and end results analysis. BMC Cancer 2022;22(1):210. doi: 10.1186/s12885-022-09217-9.