Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation

We successfully predicted the length of stay after surgery and provide explainable models with supporting analyses. In summary, we demonstrate the interpretation with the XGBoost model presenting insights on preoperative features and defining higher risk predictors to the length of stay outcome. Our development in explainable models supports the current in-depth knowledge for the future length of stay prediction on electronic medical records that aids the decision-making and facilitation of the operation department.

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Publication Details

Journal
BMC Medical Informatics and Decision Making
Published
2024-11-19
DOI
https://doi.org/10.1186/s12911-024-02755-1
Citations
11
Primary Topic
Cardiac, Anesthesia and Surgical Outcomes
Type
article
Field-Weighted Citation Impact
4.42

Funders

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article

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, Gaeun Kee, Young‐Hak Kim, Seohyun Park et al.
11 citations
BMC Medical Informatics and Decision Making
Cardiac, Anesthesia and Surgical Outcomes
4.42
article

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, Gaeun Kee, Young‐Hak Kim, Seohyun Park, Hyeram Seo, Hee Jun Kang, Imjin Ahn, Hansle Gwon, Yunha Kim, Tae Joon Jun, Heejung Choi, Jiye Han, Minkyoung Kim
article en
11 citations

Abstract

We successfully predicted the length of stay after surgery and provide explainable models with supporting analyses. In summary, we demonstrate the interpretation with the XGBoost model presenting insights on preoperative features and defining higher risk predictors to the length of stay outcome. Our development in explainable models supports the current in-depth knowledge for the future length of stay prediction on electronic medical records that aids the decision-making and facilitation of the operation department.

BMC Medical Informatics and Decision MakingVol. 24(1)
Ulsan College (KR), Asan Medical Center (KR), University of Ulsan (KR)
Korea Medical Device Development Fund, Ministry of Trade, Industry and Energy, Ministry of Food and Drug Safety, Korea Health Industry Development Institute, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 5%
Cardiac, Anesthesia and Surgical Outcomes
4.42
DOIOpen Access PDFOpenAlex
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