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December 8, 2025BloodOpen Access

A large language model-based framework for automated phenotypic characterization in myeloproliferative neoplasms

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Authors

JOJ. K. OberoiAMA. S. MuhammadUAUmair Ayub

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Overview

Automated phenotypic characterization improves diagnostic accuracy in myeloproliferative neoplasms, highlighting clinical data leveraging.

Key Points

  • This research aims to enhance diagnostic accuracy and risk stratification in myeloproliferative neoplasms using an automated framework.
  • Identified MPN patients using ICD codes from electronic health records.
  • Extracted clinical, demographic, genetic, and laboratory data from unstructured notes and reports using an automated workflow.
  • Validated the framework by comparing automated extraction performance against manually curated data.
  • Achieved 95% average accuracy in data extraction across MPN types.
  • Demonstrated 100% sensitivity and high specificity for diagnosis of PV, ET, and MF.
  • Risk stratification accuracy reached 100% for PV and ET, with a F1 score of 1.00.

Cite This Study

Oberoi et al. (2025) studied this question.

synapsesocial.com/papers/69362f694fa91c937236df7bhttps://doi.org/10.1182/blood-2025-5605
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Accuracy of Diagnosis in Myeloproliferative Neoplasms With Splanchnic Vein Thrombosis ( MPN ‐ SVT )2026
  2. 2A Proteomics-Driven Machine Learning Tool for Distinguishing ET from pre-PMF2025
  3. 3A Single Center’s Experience in the Diagnosis and Treatment of Myeloproliferative Neoplasms2025
  4. 4Multi-endpoint AI morphology model (MEAM) enhances risk prediction for vascular events and disease progression in MPNs2025
  5. 5Diagnostic conformity to who criteria and use of cytoreductive therapy in classical myeloproliferative neoplasms: Experience from a resource-limited country2025