Continual learning of longitudinal health records
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Continual learning denotes machine learning methods which can adapt to new environments while retaining and reusing knowledge gained from past experiences. Such methods address two issues encountered by models in non-stationary environments: ungeneralisability to new data, and the catastrophic forgetting of previous knowledge during retraining. This is a pervasive problem in clinical settings where patient data exhibits covariate shift not only between populations, but also continuously over time. However, while continual learning methods have seen nascent success in the imaging domain, they have been little applied to the multi-variate sequential data characteristic of patient recordings. Here we evaluate a variety of continual learning methods on longitudinal ICU data in a series of representative healthcare scenarios. We find that while several methods mitigate short-term forgetting, domain shift remains a challenging problem over a large series of tasks, with only replay based methods achieving stable long-term performance.
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Existing methodologies like continual learning or transfer learning may present potential avenues for adaptation and personalization such as for electronic health record, but their exploration and application in mobile health remain relatively underdeveloped.
“Although existing methodologies like continual learning or transfer learning may present potential avenues for adaptation and personalization such as for electronic health record [190], their exploration and application in the context of mobile health remain relatively underdeveloped.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [190]
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- Authors
- Armstrong, J., & Clifton, D. A.
- Affiliations
- University of Oxford (GB)
- Venue
- 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
- Publisher
- IEEE
- Published
- 2022-09-27
- Pages
- 01-06
- ISSN
- 2641-3590 (ISSN-L)
- OpenAlex
- W4308390386
- Cited by
- 21
- References
- 37
- Citations by year
- 2025: 7 · 2024: 6 · 2023: 6 · 2022: 2
- FWCI
- 2.90
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Engineering and Physical Sciences Research Council (EP/S02428X/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Keywords
- continual learning, catastrophic forgetting, longitudinal health records, covariate shift, domain shift, replay-based continual learning, non-stationary environments, machine learning in healthcare
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