Natural Language Processing Model for Identifying Critical Findings—A Multi-Institutional Study.
Improving detection and follow-up of recommendations made in radiology reports is a critical unmet need. The long and unstructured nature of radiology reports limits the ability of clinicians to assimilate the full report and identify all the pertinent information for prioritizing the critical cases...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 1; pp. 105 - 114 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
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=162233263&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162233263 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2023 vid: 36 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 162233263 160082253 162233263 162233263 10.1007/s10278-022-00712-w 162233263 ppf: 105 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Natural Language Processing Model for Identifying Critical Findings—A Multi-Institutional Study. aug: au: Banerjee, Imon Davis, Melissa A. Vey, Brianna L. Mazaheri, Sina Khan, Fiza Zavaletta, Vaz Gerard, Roger Gichoya, Judy Wawira Patel, Bhavik affil: Department of Radiology, Mayo Clinic, 5777 E Mayo Blvd, 85054, Phoenix, AZ, USA sug: subj: Natural Language Processing Radiology Service Documentation Methods Automation Models, Theoretical Radiology Information Systems Human Multicenter Studies Medical Records Record Review Retrospective Design ab: Improving detection and follow-up of recommendations made in radiology reports is a critical unmet need. The long and unstructured nature of radiology reports limits the ability of clinicians to assimilate the full report and identify all the pertinent information for prioritizing the critical cases. We developed an automated NLP pipeline using a transformer-based ClinicalBERT++ model which was fine-tuned on 3 M radiology reports and compared against the traditional BERT model. We validated the models on both internal hold-out ED cases from EUH as well as external cases from Mayo Clinic. We also evaluated the model by combining different sections of the radiology reports. On the internal test set of 3819 reports, the ClinicalBERT++ model achieved 0.96 f1-score while the BERT also achieved the same performance using the reason for exam and impression sections. However, ClinicalBERT++ outperformed BERT on the external test dataset of 2039 reports and achieved the highest performance for classifying critical finding reports (0.81 precision and 0.54 recall). The ClinicalBERT++ model has been successfully applied to large-scale radiology reports from 5 different sites. Automated NLP system that can analyze free-text radiology reports, along with the reason for the exam, to identify critical radiology findings and recommendations could enable automated alert notifications to clinicians about the need for clinical follow-up. The clinical significance of our proposed model is that it could be used as an additional layer of safeguard to clinical practice and reduce the chance of important findings reported in a radiology report is not overlooked by clinicians as well as provide a way to retrospectively track large hospital databases for evaluating the documentation of the critical findings. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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