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

ABSTRACT: 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 t

SUBMITTER: Banerjee I 

PROVIDER: S-EPMC9984612 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature