Dynamic Estimation of the Probability of Patient Readmission to the ICU using Electronic Medical Records

In this paper, we propose a framework to dynamically estimate the probability that a patient is readmitted after he is discharged from the ICU and transferred to a lower level care. We model this probability as a latent state which evolves over time using Dynamical Linear Models (DLM). We use as an input a combination of numerical and text features obtained from the patient Electronic Medical Records (EMRs). We process the text from the EMRs to capture different diseases, symptoms and treatments by means of noun phrases and ontologies. We also capture the global context of each text entry using Statistical Topic Models. We fill out the missing values using a Expectation Maximization based method (EM). Experimental results show that our method outperforms other methods in the literature terms of AUC, sensitivity and specificity. In addition, we show that the combination of different features (numerical and text) increases the prediction performance of the proposed approach.