Prediction of physiological subsystem failure and its impact in the prediction of patient mortality

In this paper, we present a method to dynamically predict the failure of physiological subsystems from patients admitted to the Intensive Care Unit (ICU) using heterogeneous data. We model the probability of failure in each subsystem as a latent state that evolves over time. We propose a method using Generalized Linear Dynamic models to model this latent state which is updated each time new patient data is observed. Then, we estimate the probability of patient mortality as a combination of the estimated probability of failure for different physiological subsystems. We use noun phrase extraction and statistical Topic Models to extract discriminative features which capture the patient health context that can not be obtained when only numerical features are used. We proposed a method of imputing missing values using the non-ignorable nature of the patient data. We test our proposed approach using 15,000 Electronic Medical Records (EMRs) obtained from the MIMIC II public dataset. Experimental results show that the proposed model allows us to predict subsystem failure and mortality probability with high sensitivity and specificity and detect an increase in the probability of mortality.