A multifaceted monitoring system using deep learning for hospital records and wearable sensors for home use was developed to continuously track health indicators in congestive heart failure patients.
Does a multifaceted monitoring system using deep learning on hospital records and wearable sensors predict medical outcomes in patients with congestive heart failure?
Combining deep learning on electronic health records with wearable sensor data provides a continuous monitoring framework for heart failure patients from hospitalization to home care.
In this work we present systems to monitor the health of a wide variety of high risk patients living with Congestive Heart Failure. For critical hospitalized patients, we introduce a deep learning framework for hospital records that uses a Word2Vec vector space representation to learn from a combination of structured data and unstructured text. The deep learning framework is able to assess patient risk, and accurately predict medical outcomes into the future. For less critical patients living at home, we also present algorithms for remote monitoring that can track a patient's changing health indicators using a wearable heart-rate sensors. The pool of individuals living with congestive heart failure is very diverse, which can make multifaceted approaches to health monitoring, such as those presented in this work, attractive for observing large pools of high-risk patients. Collectively the methods presented in this paper allow us to continuously monitor a person living with CHF through hospitalization, discharge, and into their home.
Fisher et al. (Fri,) conducted a other in Congestive Heart Failure. Deep learning framework and wearable heart-rate sensors was evaluated. A multifaceted monitoring system using deep learning for hospital records and wearable sensors for home use was developed to continuously track health indicators in congestive heart failure patients.