Automated Detection of Critical Results in Radiology Reports.
The goal of this study was to develop and validate text-mining algorithms to automatically identify radiology reports containing critical results including tension or increasing/new large pneumothorax, acute pulmonary embolism, acute cholecystitis, acute appendicitis, ectopic pregnancy, scrotal tors...
| Publicado en: | Journal of Digital Imaging Vol. 25; no. 1; pp. 30 - 37 |
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| Autores principales: | , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2012
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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=104634145&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104634145 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2012 vid: 25 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104634145 70531123 10.1007/s10278-011-9426-6 NLM22038514 PMC3264731 104634145 ppf: 30 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Automated Detection of Critical Results in Radiology Reports. aug: au: Lakhani, Paras Kim, Woojin Langlotz, Curtis affil: Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street Philadelphia 19106 USA sug: subj: Radiography Reports Data Mining Human Validation Studies Automation Confidence Intervals Natural Language Processing Information Retrieval Funding Source ab: The goal of this study was to develop and validate text-mining algorithms to automatically identify radiology reports containing critical results including tension or increasing/new large pneumothorax, acute pulmonary embolism, acute cholecystitis, acute appendicitis, ectopic pregnancy, scrotal torsion, unexplained free intraperitoneal air, new or increasing intracranial hemorrhage, and malpositioned tubes and lines. The algorithms were developed using rule-based approaches and designed to search for common words and phrases in radiology reports that indicate critical results. Certain text-mining features were utilized such as wildcards, stemming, negation detection, proximity matching, and expanded searches with applicable synonyms. To further improve accuracy, the algorithms utilized modality and exam-specific queries, searched under the 'Impression' field of the radiology report, and excluded reports with a low level of diagnostic certainty. Algorithm accuracy was determined using precision, recall, and F-measure using human review as the reference standard. The overall accuracy ( F-measure) of the algorithms ranged from 81% to 100%, with a mean precision and recall of 96% and 91%, respectively. These algorithms can be applied to radiology report databases for quality assurance and accreditation, integrated with existing dashboards for display and monitoring, and ported to other institutions for their own use. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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