Automatically Correlating Clinical Findings and Body Locations in Radiology Reports Using MedLEE.
In this paper, we describe and evaluate a system that extracts clinical findings and body locations from radiology reports and correlates them. The system uses Medical Language Extraction and Encoding System (MedLEE) to map the reports' free text to structured semantic representations of their conte...
| Publicado en: | Journal of Digital Imaging Vol. 25; no. 2; pp. 240 - 250 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2012
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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=104528099&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104528099 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2012 vid: 25 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104528099 72456150 10.1007/s10278-011-9411-0 NLM21796490 PMC3295967 104528099 ppf: 240 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Automatically Correlating Clinical Findings and Body Locations in Radiology Reports Using MedLEE. aug: au: Sevenster, Merlijn Ommering, Rob Qian, Yuechen affil: Philips Research Europe, Prof. Holstlaan 4 5656AA Eindhoven the Netherlands sug: subj: Reports Natural Language Processing Knowledge Bases Human Systems Design Evaluation Research Anatomy Interrater Reliability ab: In this paper, we describe and evaluate a system that extracts clinical findings and body locations from radiology reports and correlates them. The system uses Medical Language Extraction and Encoding System (MedLEE) to map the reports' free text to structured semantic representations of their content. A lightweight reasoning engine extracts the clinical findings and body locations from MedLEE's semantic representation and correlates them. Our study is illustrative for research in which existing natural language processing software is embedded in a larger system. We manually created a standard reference based on a corpus of neuro and breast radiology reports. The standard reference was used to evaluate the precision and recall of the proposed system and its modules. Our results indicate that the precision of our system is considerably better than its recall (82.32-91.37% vs. 35.67-45.91%). We conducted an error analysis and discuss here the practical usability of the system given its recall and precision performance. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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