Thesis
Electronic health records and deep learning for heart failure: from prevention to management
- Abstract:
-
Background: Heart failure (HF) is a major and growing driver of morbidity, hospitalisation, and healthcare expenditure. Despite advances in treatment, important gaps remain across the HF pathway: identifying individuals at high risk before disease onset, targeting preventive strategies more effectively, and managing the substantial heterogeneity of HF after diagnosis. Large-scale longitudinal electronic health records (EHR), together with advances in deep learning, offer new ... Expand abstract
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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
+ Rao, S
- Institution:
- University of Oxford
- Division:
- MSD
- Department:
- Women's & Reproductive Health
- Role:
- Supervisor
- ORCID:
- 0000-0001-7331-9416
+ British Heart Foundation
More from this funder
- Funder identifier:
- https://ror.org/02wdwnk04
- Funding agency for:
- Fan, Z
- Grant:
- FS/PhD/22/29321
- Programme:
- British Heart Foundation Doctoral Fellowship
- DOI:
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- University of Oxford
- Language:
-
English
- Keywords:
- Deposit date:
-
2026-09-14
- ARK identifier:
Terms of use
- Copyright holder:
- Zhengxian Fan
- Copyright date:
- 2026
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