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Utility of a large language model for extraction of clinical findings from healthcare data following lung ablation: A feasibility study. | LitMetric

AI Article Synopsis

Article Abstract

To assess the feasibility of utilizing a large language model (LLM) in extracting clinically relevant information from healthcare data in patients who have undergone lung microwave tumor ablation (MWA). In this single-center retrospective study, radiological reports and clinic notes of 20 patients were extracted, up to 12 months post-treatment. Utilizing a LLM (GPT 3.5 Turbo 16k), a zero-shot prompt strategy was employed to identify four key outcomes from relevant healthcare data: recurrence at ablation site, pneumothorax, hemoptysis and hemothorax following ablation. This was validated with ground truth labels obtained through manual chart review. Analysis of 104 radiology reports and 37 clinic notes was undertaken. The LLM output demonstrated high accuracy (85% to 100%) across the four outcomes. A LLM approach appears to have utility in extraction of clinically relevant information from healthcare data. This method may be beneficial in facilitating data analysis for future interventional radiology studies.

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Source
http://dx.doi.org/10.1016/j.jvir.2024.11.029DOI Listing

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