Event-Based Clinical Finding Extraction from Radiology Reports with Pre-trained Language Model.
Radiology reports contain a diverse and rich set of clinical abnormalities documented by radiologists during their interpretation of the images. Comprehensive semantic representations of radiological findings would enable a wide range of secondary use applications to support diagnosis, triage, outco...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 1; pp. 91 - 105 |
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| Autores principales: | , , , |
| Formato: | 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=162233267&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162233267 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: 162233267 159717567 162233267 162233267 10.1007/s10278-022-00717-5 162233267 ppf: 91 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Event-Based Clinical Finding Extraction from Radiology Reports with Pre-trained Language Model. aug: au: Lau, Wilson Lybarger, Kevin Gunn, Martin L. Yetisgen, Meliha affil: Biomedical & Health Informatics, School of Medicine, University of Washington, Seattle, WA, USA sug: subj: Radiology Service Natural Language Processing Documentation Image Interpretation, Computer Assisted Human Funding Source Tomography, X-Ray Computed Deep Learning Descriptive Statistics Random Sample Radiography, Thoracic Access to Information ab: Radiology reports contain a diverse and rich set of clinical abnormalities documented by radiologists during their interpretation of the images. Comprehensive semantic representations of radiological findings would enable a wide range of secondary use applications to support diagnosis, triage, outcomes prediction, and clinical research. In this paper, we present a new corpus of radiology reports annotated with clinical findings. Our annotation schema captures detailed representations of pathologic findings that are observable on imaging ("lesions") and other types of clinical problems ("medical problems"). The schema used an event-based representation to capture fine-grained details, including assertion, anatomy, characteristics, size, and count. Our gold standard corpus contained a total of 500 annotated computed tomography (CT) reports. We extracted triggers and argument entities using two state-of-the-art deep learning architectures, including BERT. We then predicted the linkages between trigger and argument entities (referred to as argument roles) using a BERT-based relation extraction model. We achieved the best extraction performance using a BERT model pre-trained on 3 million radiology reports from our institution: 90.9–93.4% F1 for finding triggers and 72.0–85.6% F1 for argument roles. To assess model generalizability, we used an external validation set randomly sampled from the MIMIC Chest X-ray (MIMIC-CXR) database. The extraction performance on this validation set was 95.6% for finding triggers and 79.1–89.7% for argument roles, demonstrating that the model generalized well to the cross-institutional data with a different imaging modality. We extracted the finding events from all the radiology reports in the MIMIC-CXR database and provided the extractions to the research community. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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