Assessing opioid use patient representations and subtypes

Sarah Mullin; Peter Elkin

doi:10.3233/SHTI200276

Assessing opioid use patient representations and subtypes

Department of Biomedical Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Precision medicine, diagnosis, treatment, and prevention that accounts for natural human variability, can be beneficial for complex populations. The opioid user population is heterogeneous, characterized by many disorders, medications, and procedures. Using Electronic Health Record data, we create a patient representation, finding similarities between structured data, and then cluster the patients into patient subtypes. These subtypes can then be used for subsequent analysis.

Original language	English
Title of host publication	Digital Personalized Health and Medicine - Proceedings of MIE 2020
Editors	Louise B. Pape-Haugaard, Christian Lovis, Inge Cort Madsen, Patrick Weber, Per Hostrup Nielsen, Philip Scott
Publisher	IOS Press
Pages	823-827
Number of pages	5
ISBN (Electronic)	9781643680828
DOIs	https://doi.org/10.3233/SHTI200276
State	Published - Jun 16 2020
Event	30th Medical Informatics Europe Conference, MIE 2020 - Geneva, Switzerland Duration: Apr 28 2020 → May 1 2020

Publication series

Name	Studies in Health Technology and Informatics
Volume	270
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	30th Medical Informatics Europe Conference, MIE 2020
Country/Territory	Switzerland
City	Geneva
Period	04/28/20 → 05/1/20

Keywords

Deep learning
Electronic health records
Precision medicine

Access to Document

10.3233/SHTI200276

Cite this

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title = "Assessing opioid use patient representations and subtypes",

abstract = "Precision medicine, diagnosis, treatment, and prevention that accounts for natural human variability, can be beneficial for complex populations. The opioid user population is heterogeneous, characterized by many disorders, medications, and procedures. Using Electronic Health Record data, we create a patient representation, finding similarities between structured data, and then cluster the patients into patient subtypes. These subtypes can then be used for subsequent analysis.",

keywords = "Deep learning, Electronic health records, Precision medicine",

author = "Sarah Mullin and Peter Elkin",

note = "Publisher Copyright: {\textcopyright} 2020 European Federation for Medical Informatics (EFMI) and IOS Press.; 30th Medical Informatics Europe Conference, MIE 2020 ; Conference date: 28-04-2020 Through 01-05-2020",

year = "2020",

month = jun,

day = "16",

doi = "10.3233/SHTI200276",

language = "English",

series = "Studies in Health Technology and Informatics",

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pages = "823--827",

editor = "Pape-Haugaard, \{Louise B.\} and Christian Lovis and Madsen, \{Inge Cort\} and Patrick Weber and Nielsen, \{Per Hostrup\} and Philip Scott",

booktitle = "Digital Personalized Health and Medicine - Proceedings of MIE 2020",

}

Mullin, S & Elkin, P 2020, Assessing opioid use patient representations and subtypes. in LB Pape-Haugaard, C Lovis, IC Madsen, P Weber, PH Nielsen & P Scott (eds), Digital Personalized Health and Medicine - Proceedings of MIE 2020. Studies in Health Technology and Informatics, vol. 270, IOS Press, pp. 823-827, 30th Medical Informatics Europe Conference, MIE 2020, Geneva, Switzerland, 04/28/20. https://doi.org/10.3233/SHTI200276

Assessing opioid use patient representations and subtypes. / Mullin, Sarah ; Elkin, Peter.
Digital Personalized Health and Medicine - Proceedings of MIE 2020. ed. / Louise B. Pape-Haugaard; Christian Lovis; Inge Cort Madsen; Patrick Weber; Per Hostrup Nielsen; Philip Scott. IOS Press, 2020. p. 823-827 (Studies in Health Technology and Informatics; Vol. 270).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Precision medicine, diagnosis, treatment, and prevention that accounts for natural human variability, can be beneficial for complex populations. The opioid user population is heterogeneous, characterized by many disorders, medications, and procedures. Using Electronic Health Record data, we create a patient representation, finding similarities between structured data, and then cluster the patients into patient subtypes. These subtypes can then be used for subsequent analysis.

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KW - Electronic health records

KW - Precision medicine

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A2 - Lovis, Christian

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T2 - 30th Medical Informatics Europe Conference, MIE 2020

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ER -

Assessing opioid use patient representations and subtypes

Abstract

Publication series

Conference

Keywords

Access to Document

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