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dc.contributor.authorFernández-Gutiérrez, Fen
dc.contributor.authorKennedy, JIen
dc.contributor.authorCooksey, Ren
dc.contributor.authorAtkinson, Men
dc.contributor.authorChoy, Een
dc.contributor.authorBrophy, Sen
dc.contributor.authorHuo, Len
dc.contributor.authorZhou, S-Men
dc.date.accessioned2021-11-05T11:31:39Z
dc.date.issued2021-10-15en
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>

en
dc.format.extent1908 - 1908en
dc.languageenen
dc.language.isoenen
dc.publisherMDPI AGen
dc.titleMining Primary Care Electronic Health Records for Automatic Disease Phenotyping: A Transparent Machine Learning Frameworken
dc.typeJournal Article
plymouth.issue10en
plymouth.volume11en
plymouth.journalDiagnosticsen
dc.identifier.doi10.3390/diagnostics11101908en
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
dcterms.dateAccepted2021-10-13en
dc.rights.embargodate2021-11-09en
dc.identifier.eissn2075-4418en
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.3390/diagnostics11101908en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2021-10-15en
rioxxterms.typeJournal Article/Reviewen


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