Natural Language Processing Model for Identifying Critical Findings—A Multi-Institutional Study

• Published in: Journal of digital imaging Vol. 36; no. 1; pp. 105 - 113
• Main Authors: Banerjee, Imon, Davis, Melissa A., Vey, Brianna L., Mazaheri, Sina, Khan, Fiza, Zavaletta, Vaz, Gerard, Roger, Gichoya, Judy Wawira, Patel, Bhavik
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2023; Springer Nature B.V
• Subjects: Automation; Communication; Datasets; Digital imaging; Emergency medical care; Hospitals; Humans; Imaging; Informatics; Language; Medicine; Medicine & Public Health; Natural Language Processing; Original Paper; Physicians; Radiography; Radiology; Research Report; Retrospective Studies
• ISSN: 1618-727X; 0897-1889; 1618-727X
• DOI: 10.1007/s10278-022-00712-w

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.