A computational framework for converting textual clinical diagnostic criteria into the quality data model.
Background: Constructing standard and computable clinical diagnostic criteria is an important but challenging research field in the clinical informatics community. The Quality Data Model (QDM) is emerging as a promising information model for standardizing clinical diagnostic criteria.Objective: To d...
| Published in: | Journal of Biomedical Informatics Vol. 63; pp. 11 - 22 |
|---|---|
| Main Authors: | , , , , , |
| Format: | research Journal Article |
| Published: |
Academic Press Inc.
Oct2016
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=118967219&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118967219 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Oct2016 vid: 63 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 118967219 118967219 NLM27444185 118967219 10.1016/j.jbi.2016.07.016 NLM27444185 118967219 ppf: 11 ppct: 11 formats: tig: atl: A computational framework for converting textual clinical diagnostic criteria into the quality data model. aug: au: Hong, Na Li, Dingcheng Yu, Yue Xiu, Qiongying Liu, Hongfang Jiang, Guoqian affil: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA sug: subj: Diagnosis, Computer Assisted Natural Language Processing Algorithms Human ab: Background: Constructing standard and computable clinical diagnostic criteria is an important but challenging research field in the clinical informatics community. The Quality Data Model (QDM) is emerging as a promising information model for standardizing clinical diagnostic criteria.Objective: To develop and evaluate automated methods for converting textual clinical diagnostic criteria in a structured format using QDM.Methods: We used a clinical Natural Language Processing (NLP) tool known as cTAKES to detect sentences and annotate events in diagnostic criteria. We developed a rule-based approach for assigning the QDM datatype(s) to an individual criterion, whereas we invoked a machine learning algorithm based on the Conditional Random Fields (CRFs) for annotating attributes belonging to each particular QDM datatype. We manually developed an annotated corpus as the gold standard and used standard measures (precision, recall and f-measure) for the performance evaluation.Results: We harvested 267 individual criteria with the datatypes of Symptom and Laboratory Test from 63 textual diagnostic criteria. We manually annotated attributes and values in 142 individual Laboratory Test criteria. The average performance of our rule-based approach was 0.84 of precision, 0.86 of recall, and 0.85 of f-measure; the performance of CRFs-based classification was 0.95 of precision, 0.88 of recall and 0.91 of f-measure. We also implemented a web-based tool that automatically translates textual Laboratory Test criteria into the QDM XML template format. The results indicated that our approaches leveraging cTAKES and CRFs are effective in facilitating diagnostic criteria annotation and classification.Conclusion: Our NLP-based computational framework is a feasible and useful solution in developing diagnostic criteria representation and computerization. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|