Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central Taiwan

• Published in: BMC medical informatics and decision making Vol. 22; no. 1; pp. 75 - 11
• Main Authors: Chan, Ming-Cheng, Pai, Kai-Chih, Su, Shao-An, Wang, Min-Shian, Wu, Chieh-Liang, Chao, Wen-Cheng
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 25.03.2022; BioMed Central; BMC
• Subjects: Accuracy; Albumins; Algorithms; Artificial intelligence; Artificial respiration; Calibration; Care and treatment; Complications and side effects; Coronaviruses; COVID-19; Critical care; Critical Illness; Critically ill; Datasets; Decision analysis; Decision trees; Health aspects; Health informatics; Hemoglobin; Hospitals; Humans; Infections; Intensive care; Intensive care units; Interpretability; Learning algorithms; Lime; Machine Learning; Mechanical ventilation; Mortality; Mortality prediction; Patient outcomes; Patients; Prediction models; Probability; Respiration, Artificial; Retrospective Studies; Statistical analysis; Taiwan - epidemiology; Ventilation; Ventilators; Taiwan; Critical illness; Interpretability; Mechanical ventilation; Mortality prediction; Machine learning
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-022-01817-6

Machine learning (ML) model is increasingly used to predict short-term outcome in critically ill patients, but the study for long-term outcome is sparse. We used explainable ML approach to establish 30-day, 90-day and 1-year mortality prediction model in critically ill ventilated patients. We retrospectively included patients who were admitted to intensive care units during 2015-2018 at a tertiary hospital in central Taiwan and linked with the Taiwanese nationwide death registration data. Three ML models, including extreme gradient boosting (XGBoost), random forest (RF) and logistic regression (LR), were used to establish mortality prediction model. Furthermore, we used feature importance, Shapley Additive exPlanations (SHAP) plot, partial dependence plot (PDP), and local interpretable model-agnostic explanations (LIME) to explain the established model. We enrolled 6994 patients and found the accuracy was similar among the three ML models, and the area under the curve value of using XGBoost to predict 30-day, 90-day and 1-year mortality were 0.858, 0.839 and 0.816, respectively. The calibration curve and decision curve analysis further demonstrated accuracy and applicability of models. SHAP summary plot and PDP plot illustrated the discriminative point of APACHE (acute physiology and chronic health exam) II score, haemoglobin and albumin to predict 1-year mortality. The application of LIME and SHAP force plots quantified the probability of 1-year mortality and algorithm of key features at individual patient level. We used an explainable ML approach, mainly XGBoost, SHAP and LIME plots to establish an explainable 1-year mortality prediction ML model in critically ill ventilated patients.