Patient deterioration detection using one-class classification via cluster period estimation subtask

Abstract

Deterioration is the significant degradation of the physical state prior to death. Detecting the deterioration of patients could provide an early warning to their families in instances of homecare, to clinicians treating hospitalized patients and to nurses by clients of retirement homes. Traditional supervised machine learning is not helpful for this purpose because the deterioration has individual differences for each patient, and the model cannot access the information about deterioration from healthy patients. This paper applies one-class classification (OCC) to detect deterioration changes. OCC can provide an early warning because the model can learn from only normal conditions. In particular, a one-class time-series classification (OCTSC) algorithm has been developed by combining K-means clustering with sliding windows and a linear regression subtask. The core idea is to detect the change in the signal period related to heart/breathing rate. For this purpose, clustering is applied to sliding windows, and the period is estimated using linear regression for the time index of arbitrary cluster. The deterioration change is detected by unseen scores computed as the error of linear regression subtask for cluster index.