Adverse Event Prediction by Telemonitoring and Deep Learning

Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 316)


Home health care comes as a potential solution to increasing stress on health-care systems, as well as concerns for medical patients comfort. However, additional distance from the care workers to the patients lead to more challenges, some of which can be addressed with machine learning (ML) and operations research (OR) algorithms. In this paper, we focus on automating a risk assessment of remote patients. Namely, we describe a risk prediction framework for home telemonitoring patients and show that learning a risk from weak signals in the patient’s data outperforms simple risk threshold proposed by care workers to automate the task. We combine recurrent neural networks with a ranking objective from survival analysis to evaluate the risk of patient’s adverse events. Training and testing of our methodology is achieved on a retrospective dataset gathered by an Ontario home health care agency during the course of a multi-year pilot home telemonitoring program. Results are benchmarked against alerts that were manually engineered by registered nurses, and against a simple linear baseline. This is an additional step in the application of machine learning in health care for patient-centered personalized treatments.


Home health care Telemonitoring Time-series prediction Deep learning 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.École Polytechnique de MontréalMontrealCanada
  2. 2.AlayaCareMontrealCanada

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