Unsupervised Prediction of Negative Health Events Ahead of Time. (arXiv:1901.11168v1 [cs.LG])

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 show the
effectiveness of our approach by predicting Bradycardia events in infants using
MIT-PICS dataset 1.3 minutes ahead of time with 68% AUC score on average,
using 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

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