Real time machine learning prediction of next generation sequencing test results in live clinical settings.

Journal: NPJ digital medicine
Published Date: Aug 19, 2025

Abstract

Next-generation sequencing-based tests have advanced the field of medical diagnostics, but their novelty and cost can lead to uncertainty in clinical deployment. The Heme-STAMP is one such assay that tracks mutations in genes implicated in hematolymphoid neoplasms. Rather than limiting its clinical usage or imposing rule-based criteria, we propose leveraging machine learning to guide clinical decision-making on whether this test should be ordered. We trained a machine learning model to predict the outcome of Heme-STAMP testing using 3472 orders placed between May 2018 and September 2021 from an academic medical center and demonstrated how to integrate a custom machine learning model into a live clinical environment to obtain real-time model and physician estimates. The model predicted the results of a complex next-generation sequencing test with discriminatory power comparable to expert hematologists (AUC score: 0.77 [0.66, 0.87], 0.78 [0.68, 0.86] respectively) and with the capacity to improve the calibration of human estimates.

Authors

  • Grace Y E Kim
    Department of Computer Science, Stanford, CA.
  • Matthew Schwede
    Biomedical Informatics Research, Stanford University, Stanford, CA, United States of America. Electronic address: mschwede@stanford.edu.
  • Conor K Corbin
    Center for Biomedical Informatics Research, Stanford, CA, USA; Department of Biomedical Data Science, Stanford, CA, USA.
  • Sajjad Fouladvand
    Department of Computer Science, Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA.
  • Rondeep Brar
    Department of Hematology, Stanford, CA.
  • David Iberri
    Department of Hematology, Stanford, CA.
  • William Shomali
    Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Jean S Oak
    Department of Pathology, Stanford, CA, USA.
  • Dita Gratzinger
    Department of Pathology, Stanford, CA.
  • Henning Stehr
    Department of Pathology, Stanford, CA.
  • Jonathan H Chen
    Stanford Center for Biomedical Informatics Research, Stanford, CA.

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