TY - GEN
T1 - Answering diabetic patients' questions using expert-vetted online resources
T2 - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
AU - Zeng, Yuqun
AU - Liu, Xusheng
AU - Wang, Liwei
AU - Liu, Hongfang
AU - Wang, Yanshan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/17
Y1 - 2017/1/17
N2 - As an increasing number of patients seek medical information online, it is crucial to bring expert-vetted information to patients. Multiple expert-vetted online resources exist for their accuracy and authority of medical knowledge. However, it is not clear which resource is better in meeting the information needs of the patients. Utilizing a collection of questions raised by patients with diabetes retrieved from an online forum, we manually evaluated three widely used expert-vetted online resources (WebMD, MedlinePlus, and UpToDate) in terms of content coverage and time spent to find answers. The results indicated that WebMD had slightly better content coverage with less time spent in finding answers. Leveraging the Natural Language Processing (NLP) techniques and a clinical terminology resource, the Unified Medical Language System (UMLS), we demonstrated that WebMD had a higher mapping rate of clinical concepts comparing to other resources.
AB - As an increasing number of patients seek medical information online, it is crucial to bring expert-vetted information to patients. Multiple expert-vetted online resources exist for their accuracy and authority of medical knowledge. However, it is not clear which resource is better in meeting the information needs of the patients. Utilizing a collection of questions raised by patients with diabetes retrieved from an online forum, we manually evaluated three widely used expert-vetted online resources (WebMD, MedlinePlus, and UpToDate) in terms of content coverage and time spent to find answers. The results indicated that WebMD had slightly better content coverage with less time spent in finding answers. Leveraging the Natural Language Processing (NLP) techniques and a clinical terminology resource, the Unified Medical Language System (UMLS), we demonstrated that WebMD had a higher mapping rate of clinical concepts comparing to other resources.
KW - Diabetes
KW - Expert-vetted online resources
KW - Natural language processing
UR - http://www.scopus.com/inward/record.url?scp=85013254127&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85013254127&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2016.7822575
DO - 10.1109/BIBM.2016.7822575
M3 - Conference contribution
AN - SCOPUS:85013254127
T3 - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
SP - 525
EP - 528
BT - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
A2 - Burrage, Kevin
A2 - Zhu, Qian
A2 - Liu, Yunlong
A2 - Tian, Tianhai
A2 - Wang, Yadong
A2 - Hu, Xiaohua Tony
A2 - Jiang, Qinghua
A2 - Song, Jiangning
A2 - Morishita, Shinichi
A2 - Burrage, Kevin
A2 - Wang, Guohua
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 December 2016 through 18 December 2016
ER -