Making Sense of Patient-Generated Health Data for Interpretable Patient-Centered Care: The Transition from More to Better

摘要：

　　The rise of health consumers and the accumulation of patientgenerated health data (PGHD) have brought the patient to the centerstage of precision health and behavioral science. In this positional paper we outline an interpretability-aware framework of PGHD, an important but often overlooked dimension in health services. The aim is two-fold: First, it helps generate practice-based evidence for population health management; second, it improves individual care with adaptive interventions. However, how do we check if the evidence generated from PGHD is reliable? Are the evidence directly deployable in realworld applications? How to adapt behavioral interventions for each individual patient at the touchpoint given individual patients needs? These questions commonly require better interpretability of PGHD-derived patient insights. Yet the definitions of interpretability are often underspecified. In the position paper, we outline an interpretability-aware framework to handle model properties and techniques that affect interpretability in the patientcentered care process. Throughout the positional paper, we contend that making sense of PGHD systematically in such an interpretability-aware framework is preferrable, because it improves on the trustworthiness of PGHD-derived insights and the consequent applications such as person-centered comparative effectiveness in patient-centered care.

关键词： Informatics Patient-Centered Care Machine Learning

作者: Pei-Yun Sabrina Hsueh Sanjoy Dey Subhro Das Thomas Wetter

作者单位: Center for Computational Health,Watson Research Center,Yorktown Heights,New York,USA Department of Biomedical Informatics and Medical Education,University of Washington,USA;Institute of

会议类型: 国际会议

会议名称: 第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)

会议地点: 苏州

会议语种:英文

页码: 113-117

在线出版日期: 2017-08-21（万方平台首次上网日期，不代表论文的发表时间）