Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study
Min Yang, Huiqin Zhang, Minglan Yu, Yunxuan Xu, Bo Xiang, Xiaopeng Yao · 2024
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Description
Min Yang and five co-authors test whether heart rhythm wobble can help spot depression in clinic patients. They use records from more than four hundred outpatients, split into depressed and non depressed groups. Four smart models learned from rhythm traits and basic facts.
One boosted tree model scored best in both train and test sets, with clear gains over the rest. Age, heart rate, a spread measure, and sex ranked among the top clues. A tool that shows each clue's push helps doctors read the score. The authors pitch rhythm as a low cost aid, not a stand alone test.
Catalog record
| CREATOR | Min Yang, Huiqin Zhang, Minglan Yu, Yunxuan Xu, Bo Xiang, Xiaopeng Yao |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 39 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |