An interpretable machine learning approach for predicting clinically important gastrointestinal bleeding in
Shohei Ono1, Shigehiko Uchino2, Shinshu Katayama3
1Department of Anesthesiology and Critical Care Medicine, Jichi Medical University Saitama Medical Center, Saitama, Japan; Department of Emergency and Intensive Care Medicine, Tokyo Metropolitan Tama Medical Center, Tokyo, Japan.
Insights
Machine learning accurately predicts clinically important gastrointestinal bleeding (CIGIB) in ICU patients. Key predictors identified can improve early intervention and patient outcomes.
Area of Science:
- Critical Care Medicine
- Machine Learning in Healthcare
- Predictive Analytics
Background:
- Clinically important gastrointestinal bleeding (CIGIB) is a significant complication in critically ill patients, associated with increased ICU stay and mortality.
- Existing methods for identifying high-risk patients for CIGIB are insufficient.
- No prior studies have utilized machine learning to predict CIGIB in the ICU or identify its key predictors.
Purpose of the Study:
- To develop and validate machine learning models for predicting CIGIB in intensive care unit (ICU) patients.
- To identify key clinical predictors of CIGIB using machine learning interpretability techniques.
Main Methods:
- A retrospective single-center study included 7357 ICU patients admitted between 2017 and 2024.
- Machine learning models (XGBoost, Random Forest, L1-logistic regression) were trained on data from the first 24 hours of ICU admission.
- Model performance was evaluated using AUROC, precision, recall, and F1 scores, with SHAP values used for predictor evaluation.
Main Results:
- The XGBoost model achieved the highest predictive performance with an AUROC of 0.84.
- Key predictors of CIGIB included APACHE III scores, hematocrit, APTT, creatinine, and respiratory rate.
- Invasive mechanical ventilation and stress ulcer prophylaxis were not among the top 20 predictors.
Conclusions:
- This study is the first to apply machine learning for CIGIB prediction in ICU patients, demonstrating high accuracy and interpretability.
- The developed model shows potential for guiding early interventions and prophylaxis strategies.
- Further multi-center studies and interventional trials are recommended to validate these findings.
Background:
Clinically important gastrointestinal bleeding (CIGIB) is a serious complication in critically ill patients, contributing to prolonged ICU stays and increased mortality. Despite efforts to identify high-risk patients, no previous studies have employed machine learning models to predict CIGIB during ICU stay or identify key predictors in this context.
Methods:
This single-center retrospective study included ICU patients aged 18 years or older admitted between 2017 and 2024. Patients with ICU stays of less than 24 h or GIB within 24 h of admission were excluded. Machine learning models, including XGBoost, Random Forest, and L1-regularized logistic regression, were trained using patient data from the first 24 h of ICU admission. Model performance was assessed using AUROC, precision, recall, and F1 scores. Shapley Additive Explanations (SHAP) were employed to evaluate key predictors.
Results:
A total of 7357 ICU patients were included, of whom 171 (2.3%) experienced CIGIB. The XGBoost model demonstrated the highest predictive performance with an AUROC of 0.84. Key predictors included APACHE III scores, hematocrit levels, APTT, creatinine and respiratory rate, while invasive mechanical ventilation and stress ulcer prophylaxis within the first 24 h of ICU admission did not rank among the top 20 predictors based on SHAP values.
Conclusions:
This study represents the first application of machine learning for predicting CIGIB in ICU patients, providing valuable insights into risk stratification. The model demonstrated high predictive accuracy and interpretability, highlighting its potential to guide early intervention and prophylaxis. Further multi-center studies and interventional trials are needed to validate these findings and refine clinical risk prediction strategies.
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