Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients - Deng, Yuhan; Liu, Shuang; Wang, Ziyao; Wang, Yuxin; Jiang, Yong; Liu, Baohua (Frontiers in Medicine, 2022)
Background: In-hospital mortality, prolonged length of stay (LOS) and 30-day readmission are common outcomes in intensive care unit (ICU). Traditional scoring systems and machine learning models for predicting these outcomes usually ignore the characteristics of ICU data, which are time-series forms. We aimed to use time-series deep learning models with the selective combination of three widely used scoring systems to predict these outcomes. Methods: A retrospective cohort study was conducted on 40,083 ICU patients from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Three deep learning models, including a recurrent neural network (RNN), gated recurrent unit (GRU) and long short-term memory (LSTM) with attention mechanisms, were trained for the outcomes with variables collected during the initial 24 hours after ICU admission or the last 24 hours before discharge. The inclusion of variables was based on three widely used scoring systems and the predictors consisted of time-series vital signs, laboratory tests, medication and procedures. The patients were randomly divided into a training set (80%) and a test set (20%), which were used for model development and model evaluation, respectively. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity and Brier scores were used to evaluate model performance. Variable significance was identified through attention mechanisms. Results: A total of 33 variables for 40,083 patients were enrolled for mortality and prolonged LOS prediction and 36,180 for readmission prediction. In each of the three outcomes, the performance of RNN, GRU and LSTM did not differ greatly. Mortality prediction models, prolonged LOS prediction models and readmission prediction models achieved AUCs of 0.870±0.001, 0.765±0.003 and 0.635±0.018, respectively. The top significant variables co-selected by the three deep learning models were Glasgow Coma Scale (GCS), age, blood urea nitrogen and norepinephrine for mortality; GCS, invasive ventilation and blood urea nitrogen for prolonged LOS; and blood urea nitrogen, GCS and ethnicity for readmission. Conclusions: The prognostic prediction models established in our study achieved good performance in predicting common outcomes in ICU patients, especially in mortality prediction. In addition, GCS and blood urea nitrogen were identified as the most important factors strongly associated with adverse ICU events.