Patient-level temporal aggregation for text-based asthma status ascertainment.
Objective: To specify the problem of patient-level temporal aggregation from clinical text and introduce several probabilistic methods for addressing that problem. The patient-level perspective differs from the prevailing natural language processing (NLP) practice of evaluating at the term, event, s...
| Published in: | Journal of the American Medical Informatics Association Vol. 21; no. 5; pp. 876 - 885 |
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| Main Authors: | , , , |
| Format: | research Journal Article |
| Published: |
Oxford University Press / USA
Sep2014
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103985577&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103985577 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Sep2014 vid: 21 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 103985577 NLM24833775 2012683775 10.1136/amiajnl-2013-002463 NLM24833775 PMC4147607 103985577 ppf: 876 ppct: 9 formats: tig: atl: Patient-level temporal aggregation for text-based asthma status ascertainment. aug: au: Wu, Stephen T Juhn, Young J Sohn, Sunghwan Liu, Hongfang affil: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA. sug: subj: Algorithms Asthma Classification Natural Language Processing Artificial Intelligence Asthma Diagnosis Child Classification Methods Decision Making, Computer Assisted Human Mathematics Pediatrics Time Child: 6-12 years ab: Objective: To specify the problem of patient-level temporal aggregation from clinical text and introduce several probabilistic methods for addressing that problem. The patient-level perspective differs from the prevailing natural language processing (NLP) practice of evaluating at the term, event, sentence, document, or visit level.Methods: We utilized an existing pediatric asthma cohort with manual annotations. After generating a basic feature set via standard clinical NLP methods, we introduce six methods of aggregating time-distributed features from the document level to the patient level. These aggregation methods are used to classify patients according to their asthma status in two hypothetical settings: retrospective epidemiology and clinical decision support.Results: In both settings, solid patient classification performance was obtained with machine learning algorithms on a number of evidence aggregation methods, with Sum aggregation obtaining the highest F1 score of 85.71% on the retrospective epidemiological setting, and a probability density function-based method obtaining the highest F1 score of 74.63% on the clinical decision support setting. Multiple techniques also estimated the diagnosis date (index date) of asthma with promising accuracy.Discussion: The clinical decision support setting is a more difficult problem. We rule out some aggregation methods rather than determining the best overall aggregation method, since our preliminary data set represented a practical setting in which manually annotated data were limited.Conclusion: Results contrasted the strengths of several aggregation algorithms in different settings. Multiple approaches exhibited good patient classification performance, and also predicted the timing of estimates with reasonable accuracy. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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