Machine Learning Approaches-Driven for Mortality Prediction for Patients Undergoing Craniotomy in ICU.

Objectives: We aimed to predict the mortality of patients with craniotomy in ICU by using predictive models to extract the high-risk factors leading to the death of patients from a retrospective a study.

Methods: Five machine-learning (ML) algorithms were applied for training on mortality predictive models with the data from a surgical intensive care unit (ICU) database of the Fujian Provincial Hospital in China. The accuracy, precision, recall, f1 score and the area under the receiver operator characteristic curve (AUC) were used to evaluate the performance of different models, and the calibration of the model was evaluated by brier score.

Results: We demonstrated that eXtreme Gradient Boosting (XGBoost) was more suitable for the task, demonstrating a AUC of 0.84. We analyzed the feature importance with the Local Interpretable Model-agnostic Explanations (LIME) analysis and further identified the high-risk factors of mortality in ICU through this study.

Conclusions: This study established the mortality predictive model of patients who had undergone craniotomy in ICU. Identification of the factors that had great influence on mortality has the potential to provide auxiliary decision support for clinical medical staff on their practices.