Show simple item record

dc.contributor.authorFernández-Gutiérrez, F
dc.contributor.authorKennedy, JI
dc.contributor.authorCooksey, R
dc.contributor.authorAtkinson, M
dc.contributor.authorChoy, E
dc.contributor.authorBrophy, S
dc.contributor.authorHuo, L
dc.contributor.authorZhou, Shang-Ming
dc.date.accessioned2021-11-05T11:31:39Z
dc.date.issued2021-10-15
dc.identifier.issn2075-4418
dc.identifier.issn2075-4418
dc.identifier.otherARTN 1908
dc.identifier.urihttp://hdl.handle.net/10026.1/18223
dc.description.abstract

<jats:p>(1) Background: We aimed to develop a transparent machine-learning (ML) framework to automatically identify patients with a condition from electronic health records (EHRs) via a parsimonious set of features. (2) Methods: We linked multiple sources of EHRs, including 917,496,869 primary care records and 40,656,805 secondary care records and 694,954 records from specialist surgeries between 2002 and 2012, to generate a unique dataset. Then, we treated patient identification as a problem of text classification and proposed a transparent disease-phenotyping framework. This framework comprises a generation of patient representation, feature selection, and optimal phenotyping algorithm development to tackle the imbalanced nature of the data. This framework was extensively evaluated by identifying rheumatoid arthritis (RA) and ankylosing spondylitis (AS). (3) Results: Being applied to the linked dataset of 9657 patients with 1484 cases of rheumatoid arthritis (RA) and 204 cases of ankylosing spondylitis (AS), this framework achieved accuracy and positive predictive values of 86.19% and 88.46%, respectively, for RA and 99.23% and 97.75% for AS, comparable with expert knowledge-driven methods. (4) Conclusions: This framework could potentially be used as an efficient tool for identifying patients with a condition of interest from EHRs, helping clinicians in clinical decision-support process.</jats:p>

dc.format.extent1908-1908
dc.format.mediumElectronic
dc.languageen
dc.language.isoen
dc.publisherMDPI AG
dc.subjectphenotyping
dc.subjectrheumatology
dc.subjectcohort identification
dc.subjectelectronic health records
dc.subjectfeature selection
dc.subjecttransparent machine learning
dc.subjecttext mining
dc.subjectbig data
dc.subjectartificial intelligence
dc.titleMining Primary Care Electronic Health Records for Automatic Disease Phenotyping: A Transparent Machine Learning Framework
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000715475700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue10
plymouth.volume11
plymouth.publication-statusPublished online
plymouth.journalDiagnostics
dc.identifier.doi10.3390/diagnostics11101908
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Health
plymouth.organisational-group/Plymouth/Faculty of Health/School of Nursing and Midwifery
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA03 Allied Health Professions, Dentistry, Nursing and Pharmacy
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.publisher.placeSwitzerland
dcterms.dateAccepted2021-10-13
dc.rights.embargodate2021-11-9
dc.identifier.eissn2075-4418
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.3390/diagnostics11101908
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2021-10-15
rioxxterms.typeJournal Article/Review


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record


All items in PEARL are protected by copyright law.
Author manuscripts deposited to comply with open access mandates are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author.
Theme by 
Atmire NV