Thesis
Deep learning for electronic health records: risk prediction, explainability, and uncertainty
- Abstract:
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Background: Risk models are essential for care planning and disease prevention. The unsatisfactory performance of the established clinical models has raised broad awareness and concerns. An accurate, explainable, and reliable risk model is highly beneficial but remains a challenge.
Objective: This thesis aims to develop deep learning models that can make more accurate risk predictions with the provision of uncertainty estimation and the ability...
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- Files:
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(Preview, Dissemination version, pdf, 5.8MB, Terms of use)
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Authors
Contributors
+ Rahimi, K
- Institution:
- University of Oxford
- Division:
- MSD
- Department:
- Women's & Reproductive Health
- Role:
- Supervisor
- ORCID:
- 0000-0002-4807-4610
+ Lukasiewicz, T
- Institution:
- University of Oxford
- Division:
- MPLS
- Department:
- Computer Science
- Role:
- Supervisor
- ORCID:
- 0000-0002-7644-1668
+ Mamouei, M
- Institution:
- University of Oxford
- Division:
- MSD
- Department:
- Women's & Reproductive Health
- Role:
- Supervisor
+ Alejo, A
- Institution:
- University of Oxford
- Division:
- MPLS
- Department:
- Physics
- Role:
- Examiner
+ Amitava, B
- Role:
- Examiner
+ British Heart Foundation
More from this funder
- Funder identifier:
- http://dx.doi.org/10.13039/501100000274
- Funding agency for:
- Li, Y
- Grant:
- FS/PhD/21/29110
- Programme:
- BHF Non-clinical PhD Studentship
- DOI:
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- University of Oxford
- Language:
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English
- Keywords:
- Subjects:
- Pubs id:
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2042999
- Local pid:
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pubs:2042999
- Deposit date:
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2023-07-16
- ARK identifier:
Terms of use
- Copyright holder:
- Li, Y
- Copyright date:
- 2022
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