Exploring the potential and limitations of deep learning and explainable AI for longitudinal life course analysis
These insights provide a foundation for future research applying DL and XAI to life course data, highlighting the challenges associated with sparse healthcare data, and the critical need for advancing interpretability frameworks in personalised public health.
Authors
- Seth Flaxman (ORCID: https://orcid.org/0000-0002-2477-4217)
- Neil Scheidwasser (ORCID: https://orcid.org/0000-0001-9922-0289)
- Swapnil Mishra (ORCID: https://orcid.org/0000-0002-8759-5902)
- Helen Coupland (ORCID: https://orcid.org/0000-0003-3581-1404)
- H. Juliette T. Unwin (ORCID: https://orcid.org/0000-0002-9120-4003)
- Naja Hulvej Rod (ORCID: https://orcid.org/0000-0002-6400-5105)
- Alexandros Katsiferis (ORCID: https://orcid.org/0000-0002-0957-9835)
- Megan Davies (ORCID: https://orcid.org/0000-0003-0899-6386)
- Samir Bhatt (ORCID: https://orcid.org/0000-0002-0891-4611)
Institutions
- University of Copenhagen (DK)
- University of Bristol (GB)
- University of Oxford (GB)
- National University Health System (SG)
- Imperial College London (GB)
Publication Details
- Journal
- BMC Public Health
- Published
- 2025-04-24
- DOI
- https://doi.org/10.1186/s12889-025-22705-4
- Citations
- 11
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 27.29
Funders
- London School of Hygiene and Tropical Medicine
- Schmidt Family Foundation
- National Institute for Health Research Health Protection Research Unit
- National Institute for Health and Care Research
- Department of Health and Social Care
- Imperial College London
- National Research Foundation
- National Research Foundation Singapore
- Danmarks Grundforskningsfond
- Novo Nordisk
- TrygFonden
- Novo Nordisk Fonden
- Medical Research Council
- Engineering and Physical Sciences Research Council
- European Research Council
- European and Developing Countries Clinical Trials Partnership