Combination of static and temporal data analysis to predict mortality and readmission in the intensive care

There are approximately 4 million intensive care unit (ICU) admissions each year in the United States with costs accounting for 4.1% of national health expenditures. Unforeseen adverse events contribute disproportionately to these costs. Thus, there has been substantial research in developing clinical decision support systems to predict and improve ICU outcomes such as ICU mortality, prolonged length of stay, and ICU readmission. However, the data in the ICU is collected at diverse time intervals and includes both static and temporal data. Common methods for static data mining such as Cox and logistic regression and methods for temporal data analysis such as temporal association rule mining do not model the combination of both static and temporal data. This work aims to overcome this challenge to combine static models such as logistic regression and feedforward neural networks with temporal models such as conditional random fields(CRF). We demonstrate the results using adult patient records from a publicly available database called Multi-parameter Intelligent Monitoring in Intensive Care - II (MIMIC-II). We show that the combination models outperformed individual models of logistic regression, feed-forward neural networks and conditional random fields in predicting ICU mortality. The combination models also outperform the static models of logistic regression and feed-forward neural networks for the prediction of 30 day ICU readmissions when tested using Matthews correlation coefficient and accuracy as the metrics.