Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records.
Objectives To develop classification algorithms that accurately identify axial SpA (axSpA) patients in electronic health records, and compare the performance of algorithms incorporating free-text data against approaches using only International Classification of Diseases (ICD) codes. Methods An enri...
| Publicado en: | Rheumatology Vol. 59; no. 5; pp. 1059 - 1066 |
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| Autores principales: | , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
May2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142976233&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142976233 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14620324 DN9 jtl: Rheumatology issn: 14620324 maglogo: N pubinfo: dt: May2020 vid: 59 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 142976233 142976233 142976233 10.1093/rheumatology/kez375 142976233 ppf: 1059 ppct: 7 formats: tig: atl: Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records. aug: au: Zhao, Sizheng Steven Hong, Chuan Cai, Tianrun Xu, Chang Huang, Jie Ermann, Joerg Goodson, Nicola J Solomon, Daniel H Cai, Tianxi Liao, Katherine P affil: Institute of Ageing and Chronic Disease, University of Liverpool sug: subj: Spondylarthritis Classification Natural Language Processing Electronic Health Records Human Middle Age Male Female Phenotype International Classification of Diseases Sensitivity and Specificity Middle Aged: 45-64 years Male Female ab: Objectives To develop classification algorithms that accurately identify axial SpA (axSpA) patients in electronic health records, and compare the performance of algorithms incorporating free-text data against approaches using only International Classification of Diseases (ICD) codes. Methods An enriched cohort of 7853 eligible patients was created from electronic health records of two large hospitals using automated searches (⩾1 ICD codes combined with simple text searches). Key disease concepts from free-text data were extracted using NLP and combined with ICD codes to develop algorithms. We created both supervised regression-based algorithms—on a training set of 127 axSpA cases and 423 non-cases—and unsupervised algorithms to identify patients with high probability of having axSpA from the enriched cohort. Their performance was compared against classifications using ICD codes only. Results NLP extracted four disease concepts of high predictive value: ankylosing spondylitis, sacroiliitis, HLA-B27 and spondylitis. The unsupervised algorithm, incorporating both the NLP concept and ICD code for AS, identified the greatest number of patients. By setting the probability threshold to attain 80% positive predictive value, it identified 1509 axSpA patients (mean age 53 years, 71% male). Sensitivity was 0.78, specificity 0.94 and area under the curve 0.93. The two supervised algorithms performed similarly but identified fewer patients. All three outperformed traditional approaches using ICD codes alone (area under the curve 0.80–0.87). Conclusion Algorithms incorporating free-text data can accurately identify axSpA patients in electronic health records. Large cohorts identified using these novel methods offer exciting opportunities for future clinical research. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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