Construction of in-hospital mortality predictive models

Logistic regression, decision tree, random forest, and two gradient boosting decision trees, including LightGBM, and XGBoost, were adopted to construct prediction models. In order to improve prediction, an ensemble model was constructed, which applied staking strategy using random forest, LightGBM and XGBoost [18]. The prediction probabilities of the three models were input into a logistic regression model to produce a final prediction. Hence, six in-hospital mortality predictive models were developed using logistic regression, decision tree, random forest, LightGBM, XGBoost and ensemble models, which each used 100 full features for each time window. Furthermore, the top 10 important features derived from random forest, lightGBM, and XGBoost model were also analysis [18].