Monitoring Health Changes in Congestive Heart Failure Patients using Wearables and Clinical Data

被引:2
|
作者
Fisher, Robert [1 ]
Smailagic, Asim [1 ]
Sokos, George [2 ]
机构
[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
[2] West Virginia Univ, Sch Med, Morgantown, WV USA
来源
2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) | 2017年
基金
美国安德鲁·梅隆基金会;
关键词
Intelligent monitoring; congestive heart failure; deep neural networks; Word2Vec; latent variable autoregression;
D O I
10.1109/ICMLA.2017.000-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:1061 / 1064
页数:4
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