Discerning tumor status from unstructured MRI reports -- completeness of information in existing reports and utility of automated natural language processing.
Information in electronic medical records is often in an unstructured free-text format. This format presents challenges for expedient data retrieval and may fail to convey important findings. Natural language processing (NLP) is an emerging technique for rapid and efficient clinical data retrieval....
| Publicado en: | Journal of Digital Imaging Vol. 23; no. 2; pp. 119 - 133 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2010
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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=105139695&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105139695 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2010 vid: 23 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105139695 105139695 2010587846 10.1007/s10278-009-9215-7 NLM19484309 PMC2837158 105139695 ppf: 119 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Discerning tumor status from unstructured MRI reports -- completeness of information in existing reports and utility of automated natural language processing. aug: au: Cheng LTE Zheng J Savova GK Erickson BJ affil: Department of Radiology, Mayo Clinic, Rochester 55905 USA sug: subj: Information Retrieval Natural Language Processing Neoplasms Classification Reports Data Analysis Software Disease Progression Evaluation Research Human kappa Statistic Magnetic Resonance Imaging Predictive Value of Tests Random Sample Sensitivity and Specificity ab: Information in electronic medical records is often in an unstructured free-text format. This format presents challenges for expedient data retrieval and may fail to convey important findings. Natural language processing (NLP) is an emerging technique for rapid and efficient clinical data retrieval. While proven in disease detection, the utility of NLP in discerning disease progression from free-text reports is untested. We aimed to (1) assess whether unstructured radiology reports contained sufficient information for tumor status classification; (2) develop an NLP-based data extraction tool to determine tumor status from unstructured reports; and (3) compare NLP and human tumor status classification outcomes. Consecutive follow-up brain tumor magnetic resonance imaging reports (2000- 2007) from a tertiary center were manually annotated using consensus guidelines on tumor status. Reports were randomized to NLP training (70%) or testing (30%) groups. The NLP tool utilized a support vector machines model with statistical and rule-based outcomes. Most reports had sufficient information for tumor status classification, although 0.8% did not describe status despite reference to prior examinations. Tumor size was unreported in 68.7% of documents, while 50.3% lacked data on change magnitude when there was detectable progression or regression. Using retrospective human classification as the gold standard, NLP achieved 80.6% sensitivity and 91.6% specificity for tumor status determination (mean positive predictive value, 82.4%; negative predictive value, 92.0%). In conclusion, most reports contained sufficient information for tumor status determination, though variable features were used to describe status. NLP demonstrated good accuracy for tumor status classification and may have novel application for automated disease status classification from electronic databases. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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