Using Natural Language Processing to Improve Discrete Data Capture From Interpretive Cervical Biopsy Diagnoses at a Large Health Care Organization.
* Context.--The terminology used by pathologists to describe and grade dysplasia and premalignant changes of the cervical epithelium has evolved over time. Unfortunately, coexistence of different classification systems combined with nonstandardized interpretive text has created multiple layers of in...
| Publicado en: | Archives of Pathology & Laboratory Medicine Vol. 147; no. 2; pp. 222 - 227 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
College of American Pathologists
Feb2023
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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=161730537&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161730537 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00039985 1FS jtl: Archives of Pathology & Laboratory Medicine issn: 00039985 maglogo: N pubinfo: dt: Feb2023 vid: 147 iid: 2 pid: 2550 pub: College of American Pathologists place: Northfield, Illinois artinfo: ui: 161730537 161730537 161730537 10.5858/arpa.2021-0410-OA 161730537 ppf: 222 ppct: 5 formats: fmt: @attributes: type: P tig: atl: Using Natural Language Processing to Improve Discrete Data Capture From Interpretive Cervical Biopsy Diagnoses at a Large Health Care Organization. aug: au: Wi, Soora Goldhoff, Patricia E. Fuller, Laurie A. Grewal, Kiranjit Wentzensen, Nicolas Clarke, Megan A. Lorey, Thomas S. affil: Kaiser Permanente, TPMG Regional Laboratories, Berkeley, California sug: subj: Natural Language Processing Cervical Intraepithelial Neoplasia Diagnosis Human Algorithms ab: * Context.--The terminology used by pathologists to describe and grade dysplasia and premalignant changes of the cervical epithelium has evolved over time. Unfortunately, coexistence of different classification systems combined with nonstandardized interpretive text has created multiple layers of interpretive ambiguity. Objective.--To use natural language processing (NLP) to automate and expedite translation of interpretive text to a single most severe, and thus actionable, cervical intraepithelial neoplasia (CIN) diagnosis. Design.--We developed and applied NLP algorithms to 35 847 unstructured cervical pathology reports and assessed NLP performance in identifying the most severe diagnosis, compared to expert manual review. NLP performance was determined by calculating precision, recall, and F score. Results.--The NLP algorithms yielded a precision of 0.957, a recall of 0.925, and an F score of 0.94. Additionally, we estimated that the time to evaluate each monthly biopsy file was significantly reduced, from 30 hours to 0.5 hours. Conclusions.--A set of validated NLP algorithms applied to pathology reports can rapidly and efficiently assign a discrete, actionable diagnosis using CIN classification to assist with clinical management of cervical pathology and disease. Moreover, discrete diagnostic data encoded as CIN terminology can enhance the efficiency of clinical research. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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