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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2023

Open Access 01-12-2023 | Research

Machine learning algorithms to predict intraoperative hemorrhage in surgical patients: a modeling study of real-world data in Shanghai, China

Authors: Ying Shi, Guangming Zhang, Chiye Ma, Jiading Xu, Kejia Xu, Wenyi Zhang, Jianren Wu, Liling Xu

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

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Abstract

Background

Prediction tools for various intraoperative bleeding events remain scarce. We aim to develop machine learning-based models and identify the most important predictors by real-world data from electronic medical records (EMRs).

Methods

An established database of surgical inpatients in Shanghai was utilized for analysis. A total of 51,173 inpatients were assessed for eligibility. 48,543 inpatients were obtained in the dataset and patients were divided into haemorrhage (N = 9728) and without-haemorrhage (N = 38,815) groups according to their bleeding during the procedure. Candidate predictors were selected from 27 variables, including sex (N = 48,543), age (N = 48,543), BMI (N = 48,543), renal disease (N = 26), heart disease (N = 1309), hypertension (N = 9579), diabetes (N = 4165), coagulopathy (N = 47), and other features. The models were constructed by 7 machine learning algorithms, i.e., light gradient boosting (LGB), extreme gradient boosting (XGB), cathepsin B (CatB), Ada-boosting of decision tree (AdaB), logistic regression (LR), long short-term memory (LSTM), and multilayer perception (MLP). An area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance.

Results

The mean age of the inpatients was 53 ± 17 years, and 57.5% were male. LGB showed the best predictive performance for intraoperative bleeding combining multiple indicators (AUC = 0.933, sensitivity = 0.87, specificity = 0.85, accuracy = 0.87) compared with XGB, CatB, AdaB, LR, MLP and LSTM. The three most important predictors identified by LGB were operative time, D-dimer (DD), and age.

Conclusions

We proposed LGB as the best Gradient Boosting Decision Tree (GBDT) algorithm for the evaluation of intraoperative bleeding. It is considered a simple and useful tool for predicting intraoperative bleeding in clinical settings. Operative time, DD, and age should receive attention.
Appendix
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Metadata
Title
Machine learning algorithms to predict intraoperative hemorrhage in surgical patients: a modeling study of real-world data in Shanghai, China
Authors
Ying Shi
Guangming Zhang
Chiye Ma
Jiading Xu
Kejia Xu
Wenyi Zhang
Jianren Wu
Liling Xu
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-023-02253-w

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