Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit - Zhu, Yuanda; Venugopalan, Janani; Zhang, Zhenyu; Chanani, Nikhil K.; Maher, Kevin O.; Wang, May D. (Frontiers in Artificial Intelligence, 2022)
More than 5 million patients are admitted annually to intensive care units (ICUs) in the United States. The leading causes of mortality are cardiovascular failures, multi-organ failures, and sepsis. Data driven techniques have been used on the analysis of patient data to predict adverse events such as ICU mortality and ICU readmission. These models often make use of temporal or static features from a single ICU database to make predictions on subsequent adverse events. To explore the potential of domain adaptation, we propose a method of data analysis using gradient boosting and convolutional autoencoder to predict significant adverse events in the ICU such as ICU mortality and ICU readmission. We demonstrate our results from a retrospective data analysis using patient records from a publicly available database called Multi-parameter Intelligent Monitoring in Intensive Care – II (MIMIC-II) and a local database from Children’s Healthcare of Atlanta (CHOA). We demonstrate that after adopting novel data imputation on patient ICU data, gradient boosting is effective in both mortality prediction task and ICU readmission prediction task. In addition, we use gradient boosting to identify top-ranking temporal and non-temporal features in both prediction tasks. We discuss the relationship between these features and the specific prediction task. Lastly, we indicate that convolutional autoencoder (CAE) might not be effective in feature extraction on one dataset, but domain adaptation with CAE feature extraction across two datasets shows promising results.