Application of an end-to-end model with self-attention mechanism in cardiac disease prediction
Li Li, Xi Chen, Sanjun Hu · 2024
Read Application of an end-to-end model with self-attention mechanism in cardiac disease prediction by Li Li, Xi Chen, Sanjun Hu (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-10811162.txt.
Description
Heart disease kills widely, and catching it early turns on sorting rich patient data well. Li Li, Xi Chen, and Sanjun Hu built an end-to-end learning model for that task, reported in 2025 in the catalog and dated 2023 in the text itself. The model reads heart tracings, clinic data, and medical images in one flow. A self-attention step lets the model weigh links across those inputs.
A rival-network method made extra fake training data to widen the set. On public data sets, the model topped ninety-five percent accuracy. It also found more than ninety percent of true patients, a key trait for screening. The authors say the gains come from joining data types instead of reading each alone.
Catalog record
| CREATOR | Li Li, Xi Chen, Sanjun Hu |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 109 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |