Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation
Ha Na Cho, Imjin Ahn, Hansle Gwon, Hee Jun Kang, Yunha Kim, Hyeram Seo, Heejung Choi, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Tae Joon Jun, Young-Hak Kim · 2024
Read Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation by Ha Na Cho, Imjin Ahn, Hansle Gwon, Hee Jun Kang, Yunha Kim, Hyeram Seo, Heejung Choi, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Tae Joon Jun, Young-Hak Kim (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-11577810.txt.
Description
Ha Na Cho and co-authors present work from two thousand twenty-four on predicting how long sick patients stay in hospital after surgery. The patients studied needed general anesthesia, which puts them to sleep for the operation. Knowing the likely stay in advance helps hospitals plan beds, staff, and care.
The team used records from a very large group of patients treated between two thousand eighteen and two thousand twenty. They tested four kinds of machine learning models, and one called XGBoost gave the best results, with an average error within three days. The paper also shows which factors shaped each forecast, so doctors can see why a stay was predicted to be long or short.
Catalog record
| CREATOR | Ha Na Cho, Imjin Ahn, Hansle Gwon, Hee Jun Kang, Yunha Kim, Hyeram Seo, Heejung Choi, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Tae Joon Jun, Young-Hak Kim |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 51 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |