Machine Learning-Based Cardiac Output Estimation Using Photoplethysmography in Off-Pump Coronary Artery Bypass Surgery
Cecilia A Callejas Pastor, Chahyun Oh, Boohwi Hong, Yunseo Ku, Kenichi Sakakura · 2024
Read Machine Learning-Based Cardiac Output Estimation Using Photoplethysmography in Off-Pump Coronary Artery Bypass Surgery by Cecilia A Callejas Pastor, Chahyun Oh, Boohwi Hong, Yunseo Ku, Kenichi Sakakura (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-11641833.txt.
Description
Cecilia Callejas Pastor, Chahyun Oh, Boohwi Hong, and Yunseo Ku build a no-touch way to gauge heart output in a 2024 paper. Cardiac output shows how much blood the heart pumps each minute. The standard test threads a tube into the body, which carries risk. Their method reads a light signal from a finger pulse.
Data came from patients in off-pump heart bypass surgery. A machine learning model sorted patients into low and non-low output groups. The model scored about 89 percent accuracy in training checks. In final tests it scored about 88 percent. Both output groups were judged with like skill. The authors say the tool could raise safety and comfort in care.
Catalog record
| CREATOR | Cecilia A Callejas Pastor, Chahyun Oh, Boohwi Hong, Yunseo Ku, Kenichi Sakakura |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 36 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |