Utility of a Large Language Model for Extraction of Clinical Findings from Healthcare Data following Lung Ablation: A Feasibility Study.

Journal: Journal of vascular and interventional radiology : JVIR
PMID: 39662619

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 microwave ablation for lung tumors. In this single-center retrospective study, radiology reports and clinic notes of 20 patients were extracted, up to 12 months after treatment. Utilizing an LLM (generative pretrained transformer 3.5 Turbo 16k), a zero-shot prompt strategy was employed to identify 4 key outcomes from relevant healthcare data: (a) recurrence at ablation site, (b) pneumothorax, (c) hemoptysis, and (d) 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%-100%) across the 4 outcomes. An 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.

Authors

  • Ruben Geevarghese
    Division of Interventional Radiology, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Stephen B Solomon
    Division of Interventional Radiology, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Erica S Alexander
    Division of Interventional Radiology, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Brett Marinelli
    Department of Radiology, Mount Sinai Health System, New York, New York.
  • Subrata Chatterjee
    Department of Artificial Intelligence & Machine Learning, DigITs, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Pulkit Jain
    Department of Artificial Intelligence & Machine Learning, DigITs, Memorial Sloan Kettering Cancer Center, New York, New York.
  • John Cadley
    Department of Artificial Intelligence & Machine Learning, DigITs, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Alex Hollingsworth
    Department of Artificial Intelligence & Machine Learning, DigITs, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Avijit Chatterjee
    Department of Artificial Intelligence & Machine Learning, DigITs, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Etay Ziv
    Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA.

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