Predicting ICU readmissions based on bedside medical text notes
Sérgio Curto, João Paulo Carvalho, Cátia M. Salgado, Susana Margarida Vieira, João M. C. Sousa · 2016
Patients are often discharged prematurely from Intensive Care Units (ICU) due to clinical resource limitations, economic pressure or poor discharge planning. The readmission of such patients is associated with an increased risk of death and is currently viewed as a marker for poor quality care. Several studies have focused on predicting which patients are likely to be readmitted, using techniques such as logistic regression or machine learning algorithms, and based on physiological data measured during the patients' stay at the ICU. So far, no published algorithms have been able to predict readmissions to a satisfactory degree. In this work we hypothesize that physicians' and nurses' notes could give a better explanation of both ICU discharges and readmissions, and propose using the text notes in an ICU database in order to build classification models for the prediction of readmissions. We tested the use of Fuzzy Fingerprints and other traditional text classifiers and compared them to a previously proposed model based on numerical data, obtaining very relevant improvements in the classification results, namely an AUC=0.8.