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dc.contributor.authorMarlin, Benjamin M.
dc.contributor.authorAdams, Roy J.
dc.contributor.authorSadasivam, Rajani S.
dc.contributor.authorHouston, Thomas K.
dc.date2022-08-11T08:08:32.000
dc.date.accessioned2022-08-23T15:58:27Z
dc.date.available2022-08-23T15:58:27Z
dc.date.issued2013-11-16
dc.date.submitted2015-08-10
dc.identifier.citationAMIA Annu Symp Proc. 2013 Nov 16;2013:1600-7. eCollection 2013.
dc.identifier.issn1559-4076 (Linking)
dc.identifier.pmid24551430
dc.identifier.urihttp://hdl.handle.net/20.500.14038/30436
dc.description.abstractThe goal of computer tailored health communications (CTHC) is to promote healthy behaviors by sending messages tailored to individual patients. Current CTHC systems collect baseline patient "profiles" and then use expert-written, rule-based systems to target messages to subsets of patients. Our main interest in this work is the study of collaborative filtering-based CTHC systems that can learn to tailor future message selections to individual patients based explicit feedback about past message selections. This paper reports the results of a study designed to collect explicit feedback (ratings) regarding four aspects of messages from 100 subjects in the smoking cessation support domain. Our results show that most users have positive opinions of most messages and that the ratings for all four aspects of the messages are highly correlated with each other. Finally, we conduct a range of rating prediction experiments comparing several different model variations. Our results show that predicting future ratings based on each user's past ratings contributes the most to predictive accuracy.
dc.language.isoen_US
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=24551430&dopt=Abstract">Link to Article in PubMed</a>
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3900169/
dc.subject*Attitude to Health
dc.subjectHealth Communication
dc.subjectHealth Education
dc.subjectHumans
dc.subjectInternet
dc.subjectModels, Theoretical
dc.subject*Smoking Cessation
dc.subjectHealth Information Technology
dc.subjectHealth Services Research
dc.subjectPublic Health
dc.titleTowards collaborative filtering recommender systems for tailored health communications
dc.typeJournal Article
dc.source.journaltitleAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
dc.source.volume2013
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/faculty_pubs/709
dc.identifier.contextkey7435824
html.description.abstract<p>The goal of computer tailored health communications (CTHC) is to promote healthy behaviors by sending messages tailored to individual patients. Current CTHC systems collect baseline patient "profiles" and then use expert-written, rule-based systems to target messages to subsets of patients. Our main interest in this work is the study of collaborative filtering-based CTHC systems that can learn to tailor future message selections to individual patients based explicit feedback about past message selections. This paper reports the results of a study designed to collect explicit feedback (ratings) regarding four aspects of messages from 100 subjects in the smoking cessation support domain. Our results show that most users have positive opinions of most messages and that the ratings for all four aspects of the messages are highly correlated with each other. Finally, we conduct a range of rating prediction experiments comparing several different model variations. Our results show that predicting future ratings based on each user's past ratings contributes the most to predictive accuracy.</p>
dc.identifier.submissionpathfaculty_pubs/709
dc.contributor.departmentDepartment of Quantitative Health Sciences
dc.source.pages1600-7


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