An explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery.

ABSTRACT:

Aim

The study aimed to develop a predictive model with machine learning (ML) algorithm, to predict and manage the need for red blood cell (RBC) transfusion during hip fracture surgery.

Methods

Data of 2785 cases that underwent hip fracture surgery from April 2016 to May 2022 were collected, covering demographics, medical history and comorbidities, type of surgery and preoperative laboratory results. The primary outcome was the intraoperative RBC transfusion. The predicting performance of six algorithms were respectively evaluated with the area under the receiver operating characteristic (AUROC). The SHapley Additive exPlanations (SHAP) package was applied to interpret the Random Forest (RF) model. Data from 122 patients at The Third Affiliated Hospital of Sun Yat-sen Univers

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC11656766 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature