Predicting hospital associated disability from imbalanced data using supervised learning

Abstract

Hospitalization of elderly patients can lead to serious adverse effects on their functional capability. Identifying the underlying factors leading to such adverse effects is an active area of medical research. The purpose of the current paper is to show the potential of artificial intelligence in the form of machine learning to complement the existing medical research. This is accomplished by studying the outcome of hospitalization of elderly patients as a supervised learning task. A rich set of features characterizing the medical and social situation of elderly patients is leveraged and using confusion matrices, association rule mining, and two different classes of supervised learning algorithms, it is shown that the need for help and supervision are the most important features predicting whether these patients will return home after hospitalization. Such findings can help to improve hospitalization and rehabilitation of elderly patients.