A Personal Health Recommender System Incorporating Personal Health Records, Modular Ontologies, and Crowd-Sourced Data

被引：0

作者：

Hu, Hengyi ^{[1
]}

Elkus, Adam ^{[2
]}

Kerschberg, Larry ^{[3
]}

机构：

[1] George Mason Univ, Dept Informat Sci & Technol, Fairfax, VA 22030 USA

[2] George Mason Univ, Dept Computat Social Sci, Fairfax, VA 22030 USA

[3] George Mason Univ, Dept Comp Sci, Fairfax, VA 22030 USA

来源：

PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016 | 2016年

关键词：

Crowdsourcing; HealthIT; health informatics; modular ontology; ontology; recommender system; social networks;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present an architecture for a Personal Health Recommender System (PHRS) that begins with a person's personal electronic health record (PEHR) and augments it by combining crowd-sourced data mined for symptoms, diseases, treatments and best practices, together with authoritative sources and curated domain ontologies. This novel approach is patient-centric in that the PEHR contains the patient's health stats, specific symptoms, diagnoses, illnesses, medications and treatment plans. By accessing anonymized crowd-sourced data for similar cases, the patient and healthcare providers can ascertain the best course of treatment. In addition, the data in the PEHR can be classified according to authoritative ontologies using Semantic Web services. A modular ontology may be constructed for each of the patient's illnesses, and then they may be combined into an integrated ontology by patient or illness. As the PEHR is populated with more data, the ontology may evolve, guided by resources and services available on the Semantic Web.

引用

页码：1027 / 1033

页数：7