家庭血压测量决策支持系统

Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare Pub Date : 2017-05-23 DOI:10.1145/3154862.3154891

Akshay Jain, M. Popescu, J. Keller, Jeffery L. Belden, R. Koopman, Sonal J. Patil, Shannon M. Canfield, L. Steege, Victoria A. Shaffer, P. Wegier, K. Valentine, A. Hathaway

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引用次数: 1

摘要

可穿戴和非穿戴式传感器无处不在。然而，它们提供的数据对用户的健康影响并不总是很清楚。在本文中，我们提出了一个决策支持系统(DSS)，帮助家庭血压(HBP)监测仪的用户决定及时咨询医生。虽然HBP比办公室读数更可靠，但由于食物、锻炼或记录测量误差等因素，它的变化更大。我们的决策支持系统是基于由数据的语言摘要组成的模糊规则。这些规则是根据现行的美国临床指南设计的，并使用进化算法进行调整。在监测超过3个月的40例患者的数据集中，我们获得了医生和使用他们的数据训练的DSS之间的解释一致性为0.97，而这些医生之间的平均一致性为0.95。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A decision support system for home BP measurements

Wearable and non-wearable sensors are pervasive. However, the health implications of the data they provide is not always clear for the user. In this paper we present a Decision Support System (DSS) that assists a user of a Home Blood Pressure (HBP) monitor to decide timely consultation with a doctor. While HBP is more reliable than office readings, it is more variable due to factors such as food, exercise or error in recording measurements. Our DSS is based on fuzzy rules composed of linguistic summaries of the data. The rules are designed from the current US clinical guidelines and are tuned using an evolutionary algorithm. On a dataset of 40 patients monitored over 3 months, we obtained an interrater agreement of 0.97 between the physicians and DSS trained with their data, while the average agreement between these same physicians was 0.95.

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Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare

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