Ontology-guided machine learning outperforms zero-shot foundation models for cardiac ultrasound text reports.

Journal: Scientific reports
PMID: 39953053

Abstract

Big data can revolutionize research and quality improvement for cardiac ultrasound. Text reports are a critical part of such analyses. Cardiac ultrasound reports include structured and free text and vary across institutions, hampering attempts to mine text for useful insights. Natural language processing (NLP) can help and includes both statistical- and large language model based techniques. We tested whether we could use NLP to map cardiac ultrasound text to a three-level hierarchical ontology. We used statistical machine learning (EchoMap) and zero-shot inference using GPT. We tested eight datasets from 24 different institutions and compared both methods against clinician-scored ground truth. Despite all adhering to clinical guidelines, institutions differed in their structured reporting. EchoMap performed best with validation accuracy of 98% for the first ontology level, 93% for first and second levels, and 79% for all three. EchoMap retained performance across external test datasets and could extrapolate to examples not included in training. EchoMap's accuracy was comparable to zero-shot GPT at the first level of the ontology and outperformed GPT at second and third levels. We show that statistical machine learning can map text to structured ontology and may be especially useful for small, specialized text datasets.

Authors

  • Suganya Subramaniam
    University of California, San Francisco, 521 Parnassus Avenue Rm 6222, San Francisco, CA, 94143, USA.
  • Sara Rizvi
    University of California, San Francisco, 521 Parnassus Avenue Rm 6222, San Francisco, CA, 94143, USA.
  • Ramya Ramesh
    University of California, Berkeley, Berkeley, CA, USA.
  • Vibhor Sehgal
    University of California, Berkeley, Berkeley, CA, USA.
  • Brinda Gurusamy
    University of California, Berkeley, Berkeley, CA, USA.
  • Hikmatullah Arif
    University of Washington, Seattle, WA, USA.
  • Jeffrey Tran
    University of Arizona, Tucson, AZ, USA.
  • Ritu Thamman
    University of Pisburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
  • Emeka C Anyanwu
    University of Pennsylvania, Philadelphia, PA, USA.
  • Ronald Mastouri
    Indiana University, Indianapolis, IN, USA.
  • G Burkhard Mackensen
    University of Washington, Seattle, WA, USA.
  • Rima Arnaout

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