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Heart failure hospitalization prediction in remote patient management systems

机译：远程患者管理系统中的心力衰竭住院预测

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Healthcare systems are shifting from patient care in hospitals to monitored care at home. It is expected to improve the quality of care without exploding the costs. Remote patient management (RPM) systems offer a great potential in monitoring patients with chronic diseases, like heart failure or diabetes. Patient modeling in RPM systems opens opportunities in two broad directions: personalizing information services, and alerting medical personnel about the changing conditions of a patient. In this study we focus on heart failure hospitalization (HFH) prediction, which is a particular problem of patient modeling for alerting. We formulate a short term HFH prediction problem and show how to address it with a data mining approach. We emphasize challenges related to the heterogeneity, different types and periodicity of the data available in RPM systems. We present an experimental study on HFH prediction using, which results lay a foundation for further studies and implementation of alerting and personalization services in RPM systems.
机译：医疗保健系统正在从医院的病人护理转变为在家中受监控的护理。期望在不增加成本的情况下提高护理质量。远程患者管理（RPM）系统在监视患有慢性疾病（如心力衰竭或糖尿病）的患者方面具有巨大的潜力。 RPM系统中的患者建模为两个广泛的方向带来了机遇：个性化信息服务，以及向医务人员警告患者不断变化的状况。在这项研究中，我们专注于心力衰竭住院（HFH）预测，这是对患者模型进行预警的一个特殊问题。我们制定了短期HFH预测问题，并展示了如何使用数据挖掘方法来解决它。我们强调与RPM系统中可用数据的异构性，不同类型和周期性有关的挑战。我们提出了有关HFH预测的实验研究，该结果为进一步研究和在RPM系统中实施警报和个性化服务奠定了基础。

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