Interpretable deep learning survival predictions in sporadic Creutzfeldt–Jakob disease
sCJD survival can be predicted using routinely collected clinical data at diagnosis. Our analysis pipeline has similar levels of performance to classical methods and provide clinically meaningful interpretation which help deepen clinical understanding of the condition. Further development and clinical validation will facilitate improvements in prognostication, care planning, and stratification to clinical trials.
Authors
- Marcelo A. Barria (ORCID: https://orcid.org/0000-0003-4789-7401)
- Hatice Kurucu (ORCID: https://orcid.org/0000-0001-9602-4346)
- Alison J. E. Green (ORCID: https://orcid.org/0000-0003-1858-6751)
- David Summers (ORCID: https://orcid.org/0000-0002-3365-046X)
- Colin Smith (ORCID: https://orcid.org/0000-0002-4507-5132)
- Sohan Seth (ORCID: https://orcid.org/0000-0002-7464-4752)
- J. Mackenzie (ORCID: https://orcid.org/0000-0002-1773-2684)
- John Centola (ORCID: https://orcid.org/0000-0003-3696-8403)
- Neil Watson (ORCID: https://orcid.org/0000-0001-7355-4643)
- Suvankar Pal (ORCID: https://orcid.org/0000-0003-4276-639X)
- Johnny Tam (ORCID: https://orcid.org/0000-0002-6976-2666)
Institutions
- University of Edinburgh (GB)
Publication Details
- Journal
- Journal of Neurology
- Published
- 2024-12-16
- DOI
- https://doi.org/10.1007/s00415-024-12815-1
- Citations
- 3
- Primary Topic
- Prion Diseases and Protein Misfolding
- Type
- article
- Field-Weighted Citation Impact
- 0.70
Funders
- National Institute for Health and Care Research
- Department of Health and Social Care