An In-Hospital Mortality Risk Model for Patients Undergoing Coronary Artery Bypass Grafting Based on Machine Learning: Cohort Study
Kun Zhu, Wenyuan Lu, Shui Liu, Hongyuan Lin, Jianfeng Hou · 2026
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Description
Kun Zhu and co-authors train learning models to predict death in hospital after bypass surgery in a 2026 cohort study. Bypass grafting stays the main surgery for blocked heart arteries. Older risk scores miss some of what modern data can catch.
The study drew 21,443 patients from a national Chinese surgery registry covering 2017 to 2020. Fifteen algorithms were built and checked against two standard risk scores. A boosting model won, beating both scores on the held-back test group. Death in hospital ran near two in every hundred patients. Better forecasts can guide consent talks and focus watchful care on the frailest patients.
Catalog record
| CREATOR | Kun Zhu, Wenyuan Lu, Shui Liu, Hongyuan Lin, Jianfeng Hou |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |