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Related Experiment Video

Updated: Nov 10, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Predicting Survival in Veterans with Follicular Lymphoma Using Structured Electronic Health Record Information and

Chunyang Li1,2, Vikas Patil1,2, Kelli M Rasmussen1,2

  • 1Veritas, Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA.

International Journal of Environmental Research and Public Health
|April 3, 2021
PubMed
Summary

Machine learning accurately predicts follicular lymphoma (FL) survival using electronic health records (EHR), offering earlier insights than the standard progression of disease at 24 months (POD24) metric.

Keywords:
electronic health recordsfollicular lymphomahealthcaremachine learningmedical and health datapredictive analyticsprognosisrandom survival forestsurvival analysisveterans health administration

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Area of Science:

  • Oncology
  • Medical Informatics
  • Biostatistics

Background:

  • Follicular lymphoma (FL) prognosis typically relies on Progression of Disease at 24 months (POD24), requiring two years of observation post-first-line therapy (L1).
  • Predicting individual patient outcomes earlier is crucial for timely treatment adjustments and improved management.

Purpose of the Study:

  • To develop and evaluate a machine learning model using electronic health record (EHR) data to predict individual survival at the initiation of first-line therapy (L1) for follicular lymphoma patients.
  • To compare the predictive performance of machine learning models against traditional prognostic methods.

Main Methods:

  • A nationwide cohort of 523 follicular lymphoma patients diagnosed between 2006-2014 within the Veterans Health Administration was analyzed.
  • Data included curated prognostic variables, laboratory results (labs), and International Classification of Diseases (ICD) diagnostic codes.
  • Random survival forests (RSF) and Cox models were compared using datasets combining curated variables with labs and ICD codes. Performance was assessed using area under the receiver operating characteristic curve (AUC).

Main Results:

  • The best performing model, RSF using curated variables plus labs, achieved a mean AUC of 0.73 (95% CI: 0.71-0.75).
  • This model closely approximated, but did not surpass, the performance of a Cox model using POD24 (mean AUC 0.74 [95% CI: 0.71-0.77]).
  • RSF models utilizing EHR data demonstrated superior performance compared to traditional prognostic variables alone.

Conclusions:

  • Machine learning applied to structured EHR data can effectively predict follicular lymphoma patient survival at L1 initiation.
  • This approach offers a promising foundation for integrating predictive algorithms into EHR systems, providing clinicians with earlier prognostic information.
  • The developed tool approximates the predictive power of POD24 using data available significantly earlier in the disease course.