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

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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	English
Title of host publication	Proceedings of the 2017 International Conference on Digital Health
Publisher	ACM
Pages	157-161
ISBN (Print)	9781450352499
DOIs	https://doi.org/10.1145/3079452.3079499
Publication status	Published - 2017
Externally published	Yes

Publication series

Name	Digital Health 2017
Publisher	Association for Computing Machinery

Keywords

patient modeling
health informatics
health recommender systems
disease modeling
online health interventions

Access to Document

10.1145/3079452.3079499

Cite this

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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.",

keywords = "patient modeling, health informatics, health recommender systems, disease modeling, online health interventions",

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

year = "2017",

doi = "10.1145/3079452.3079499",

language = "English",

isbn = "9781450352499",

series = "Digital Health 2017",

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pages = "157--161",

booktitle = "Proceedings of the 2017 International Conference on Digital Health",

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TY - GEN

T1 - Towards Health (Aware) Recommender Systems

AU - Schäfer, Hanna

AU - Hors-Fraile, Santiago

AU - Karumur, Raghav Pavan

AU - Calero Valdez, André

AU - Said, Alan

AU - Torkamaan, Helma

AU - Ulmer, Tom

AU - Trattner, Christoph

PY - 2017

Y1 - 2017

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.

KW - patient modeling

KW - health informatics

KW - health recommender systems

KW - disease modeling

KW - online health interventions

U2 - 10.1145/3079452.3079499

DO - 10.1145/3079452.3079499

M3 - Conference contribution

SN - 9781450352499

T3 - Digital Health 2017

SP - 157

EP - 161

BT - Proceedings of the 2017 International Conference on Digital Health

PB - ACM

ER -

Towards Health (Aware) Recommender Systems

Abstract

Publication series

Keywords

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Cite this