World journal of emergency medicine · 2026
Development and validation of an interpretable machine learning model to predict high-risk patients with acute small bowel obstruction.
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Zhao Yingsong, Wang Hongda, Zhao Eryang, Zhao Guangyu, Zhang Junrong, Chen Sanyang, Li Yang, Zhao Yong, Zhuo Wenfeng, Lyu Guozheng, Hu Ping, Zhao Gang
Аннотация
BACKGROUND: Small bowel obstruction (SBO) is a common emergency surgical disease for which identifying patients need emergency surgical resection due to nonviable small bowel tissue poses a significant challenge. This study aimed to develop and validate a predictive model to guide surgical decision-making using readily available clinical data.
METHODS: A retrospective cohort study was conducted using data from Wuhan Union Hospital between 2017 and 2023 to establish the prediction model, with an external validation cohort from two other medical centers. Six machine learning algorithms (Logistic Regression [LR], Random Forest [RF], K Nearest-Neighbours [KNN], Multilayer Perceptron [MLP], Adaptive Boosting [AdaBoost] and eXtreme Gradient Boosting [XGBoost]) were employed, and the final model was based on LR classifier. Performance was evaluated with various metrics including AUC-ROC, accuracy, and decision curve analysis. SHapley Additive exPlanations (SHAP) method was used for model interpretation.
RESULTS: High-risk and low-risk SBO groups differed significantly in clinical, laboratory, imaging, treatment, and outcome variables. Ten predictors were retained: white blood cell count, history of abdominal operation, platelet, albumin, neutrophil, spiral sign, acute bellyache, ascites, lymphocyte count, and rebound tenderness. After comparing the six algorithms, LR was selected because it showed the most consistent generalization and calibration. The final LR model achieved an AUC of 0.889 (95% CI 0.855-0.923) in the training data, a mean cross-validation AUC of 0.876 (95% CI 0.773-0.978), and an AUC of 0.873 (95% CI 0.818-0.929) in the independent test set. In the external validation cohort from the two other medical centers, the model achieved an AUC of 0.762 (95% CI 0.693-0.831).
CONCLUSION: An interpretable machine learning model was developed and validated for prediction of high-risk SBO patients upon admission. The model may offer decision support for clinicians, aiding in risk stratification and guiding treatment strategies.
METHODS: A retrospective cohort study was conducted using data from Wuhan Union Hospital between 2017 and 2023 to establish the prediction model, with an external validation cohort from two other medical centers. Six machine learning algorithms (Logistic Regression [LR], Random Forest [RF], K Nearest-Neighbours [KNN], Multilayer Perceptron [MLP], Adaptive Boosting [AdaBoost] and eXtreme Gradient Boosting [XGBoost]) were employed, and the final model was based on LR classifier. Performance was evaluated with various metrics including AUC-ROC, accuracy, and decision curve analysis. SHapley Additive exPlanations (SHAP) method was used for model interpretation.
RESULTS: High-risk and low-risk SBO groups differed significantly in clinical, laboratory, imaging, treatment, and outcome variables. Ten predictors were retained: white blood cell count, history of abdominal operation, platelet, albumin, neutrophil, spiral sign, acute bellyache, ascites, lymphocyte count, and rebound tenderness. After comparing the six algorithms, LR was selected because it showed the most consistent generalization and calibration. The final LR model achieved an AUC of 0.889 (95% CI 0.855-0.923) in the training data, a mean cross-validation AUC of 0.876 (95% CI 0.773-0.978), and an AUC of 0.873 (95% CI 0.818-0.929) in the independent test set. In the external validation cohort from the two other medical centers, the model achieved an AUC of 0.762 (95% CI 0.693-0.831).
CONCLUSION: An interpretable machine learning model was developed and validated for prediction of high-risk SBO patients upon admission. The model may offer decision support for clinicians, aiding in risk stratification and guiding treatment strategies.