TY - GEN
T1 - Towards Health (Aware) Recommender Systems.
AU - Schäfer, Hanna
AU - Hors-Fraile, Santiago
AU - Karumur, Raghav Pavan
AU - Valdez, André Calero
AU - Said, Alan
AU - Torkamaan, Helma
AU - Ulmer, Tom
AU - Trattner, Christoph
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - People increasingly use the Internet for obtaining information regarding diseases, diagnoses and available treatments. Currently, many online health portals already provide non-personalized health information in the form of articles. However, it can be challenging to find information relevant to one's condition, interpret this in context, and understand the medical terms and relationships. Recommender Systems (RS) already help these systems perform precise information filtering. In this short paper, we look one step ahead and show the progress made towards RS helping users find personalized, complex medical interventions or support them with preventive healthcare measures. We identify key challenges that need to be addressed for RS to offer the kind of decision support needed in high-risk domains like healthcare.
AB - People increasingly use the Internet for obtaining information regarding diseases, diagnoses and available treatments. Currently, many online health portals already provide non-personalized health information in the form of articles. However, it can be challenging to find information relevant to one's condition, interpret this in context, and understand the medical terms and relationships. Recommender Systems (RS) already help these systems perform precise information filtering. In this short paper, we look one step ahead and show the progress made towards RS helping users find personalized, complex medical interventions or support them with preventive healthcare measures. We identify key challenges that need to be addressed for RS to offer the kind of decision support needed in high-risk domains like healthcare.
UR - https://dblp.org/db/conf/ehealth/dh2017.html#SchaferHKVSTUT17
UR - https://dblp.org/db/conf/ehealth/2017
U2 - 10.1145/3079452.3079499
DO - 10.1145/3079452.3079499
M3 - Conference contribution
SN - 978-1-4503-5249-9
SP - 157
EP - 161
BT - DH '17: Proceedings of the 2017 International Conference on Digital Health
PB - Association for Computing Machinery
ER -