Towards health (Aware) recommender systems

Hanna Schäfer; Santiago Hors-Fraile; Raghav Pavan Karumur; André Calero Valdez; Alan Said; Helma Torkamaan; Tom Ulmer; Christoph Trattner

doi:10.1145/3079452.3079499

Towards health (Aware) recommender systems

Hanna Schäfer, Santiago Hors-Fraile, Raghav Pavan Karumur, André Calero Valdez, Alan Said, Helma Torkamaan, Tom Ulmer, Christoph Trattner

Institute of Human-Centered Interactive Systems

124 Citations (Scopus)

Abstract

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.

Original language	German
Title of host publication	7th International Conference on Digital Health, DH 2017 : ACM International Conference Proceeding Series
Publisher	ACM
Publication date	02.07.2017
Pages	157-161
DOIs	https://doi.org/10.1145/3079452.3079499
Publication status	Published - 02.07.2017

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 4 Quality Education
SDG 8 Decent Work and Economic Growth
SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities
SDG 13 Climate Action
SDG 16 Peace, Justice and Strong Institutions

Access to Document

10.1145/3079452.3079499

Cite this

@inproceedings{2e96e583d4bb4acfbad7ce70c3a64037,

title = "Towards health (Aware) recommender systems",

abstract = "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.",

author = "Hanna Sch{\"a}fer and Santiago Hors-Fraile and Karumur, \{Raghav Pavan\} and Valdez, \{Andr{\'e} Calero\} and Alan Said and Helma Torkamaan and Tom Ulmer and Christoph Trattner",

note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.",

year = "2017",

month = jul,

day = "2",

doi = "10.1145/3079452.3079499",

language = "Deutsch",

pages = "157--161",

booktitle = "7th International Conference on Digital Health, DH 2017",

publisher = "ACM",

}

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

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.

U2 - 10.1145/3079452.3079499

DO - 10.1145/3079452.3079499

M3 - Konferenzbeitrag

SP - 157

EP - 161

BT - 7th International Conference on Digital Health, DH 2017

PB - ACM

ER -