Proactive vs Reactive Machine Learning in Health Care: Lessons From the COVID-19 Pandemic
Abstract
Baptiste Vasey, MMed; Stephan Ursprung, MMed; Benjamin Beddoe, BSc; Elliott H. Taylor, BSc; Neale Marlow, MBBS; Nicole Bilbro, MD; Peter Watkinson, MD; Peter McCulloch, MD
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Incorporating disparate data lends itself to seemingly better predictions.
“While incorporating disparate data does lend itself to seemingly better predictions139, as knowledge around certain diseases accumulates, data fusion in healthcare is an evolving target that warrants proactively adapting to the dynamic landscape190.”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [190]
Cites
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works
Interleukin-6 receptor antagonists in critically ill patients with Covid-19.
The REMAP-CAP Investigators (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2100433 · Cited by 1,962 · see citing works
Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00307-0 · Cited by 1,044 · see citing works
Roberts, M. T., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Avilés-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2020) · Apollo (University of Cambridge) · doi:10.17863/cam.64000 · Cited by 902 · see citing works
The Clinician and Dataset Shift in Artificial Intelligence
Finlayson, S. G., Subbaswamy, A., Singh, K., Bowers, J., Kupke, A., Zittrain, J., Kohane, I. S., & Saria, S. (2021) · New England Journal of Medicine · doi:10.1056/nejmc2104626 · Cited by 855 · see citing works
Dreisbach, C., Koleck, T. A., Bourne, P. E., & Henry, S. B. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.02.008 · Cited by 231 · see citing works
Writing Committee for the REMAP-CAP Investigators, Abdelhady, H., Abdelrazik, M., Abdi, Z., Abdo, D., Abdulle, A., Abel, L., Abouzeenni, S., Abrahamson, G., Abusamra, Y., et al. (2021) · JAMA · doi:10.1001/jama.2021.18178 · Cited by 212 · see citing works
Efficient and targeted COVID-19 border testing via reinforcement learning
Bastani, H., Drakopoulos, K., Gupta, V., Vlachogiannis, I. A., Hadjichristodoulou, C. S., Λάγιου, Π., Magiorkinis, G., Paraskevis, D. N., & Tsiodras, S. (2021) · Nature · doi:10.1038/s41586-021-04014-z · Cited by 100 · see citing works
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Multimodal machine learning in precision health: A scoping review
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00712-8 · Cited by 528 · see citing works
Self-citation
- Authors
- Luo, Y., Wunderink, R. G., & Lloyd-Jones, D.
- Affiliations
- Northwestern University (US)
- Division of Cardiology, Department of Medicine, and Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Corresponding author
- Luo, Yuan
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2022-02-15
- Volume
- 327
- Issue
- 7
- Pages
- 623
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- OpenAlex
- W4210724916
- Cited by
- 24
- References
- 8
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 4 · 2023: 6 · 2022: 10
- FWCI
- 1.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (U01TR003528)
- National Institute of Allergy and Infectious Diseases (U19AI135964)
- U.S. National Library of Medicine (R01LM013337)
- National Center for Advancing Translational Sciences
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- health care, clinical decision support, predictive analytics
- MeSH terms
- Machine Learning, COVID-19, Delivery of Health Care, Humans
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