Unsupervised Prediction of Negative Health Events Ahead of Time

The emergence of continuous health monitoring and the availability of an enormous amount of time series data has provided a great opportunity for the advancement of personal health tracking. In recent years, unsupervised learning methods have drawn special attention of researchers to tackle the sparse annotation of health data and real-time detection of anomalies has been a central problem of interest. However, one problem that has not been well addressed before is the early prediction of forthcoming negative health events. Early signs of an event can introduce subtle and gradual changes in the health signal prior to its onset, detection of which can be invaluable in effective prevention. In this study, we first demonstrate our observations on the shortcoming of widely adopted anomaly detection methods in uncovering the changes prior to a negative health event. We then propose a framework which relies on online clustering of signal segment representations which are automatically learned by a specially designed LSTM auto-encoder. We benchmark our results on the publicly available MIT-PICS dataset and show the effectiveness of our approach by predicting Bradycardia events in infants 1.3 minutes ahead of time with 68% AUC score on average, with no label supervision. Results of our study can indicate the viability of our approach in the early detection of health events in other applications as well.