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Distributed learning on 20 000+ lung cancer patients - The Personal Health Train.

Timo M Deist1, Frank J W M Dankers2, Priyanka Ojha3

  • 1Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands; The D-Lab: Dpt of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands.

Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology
|January 9, 2020
PubMed
Summary

The Personal Health Train (PHT) enables secure, distributed analysis of lung cancer patient data across international institutions. This privacy-preserving infrastructure facilitates scientific progress by overcoming data sharing barriers.

Keywords:
Big dataDistributed learningFAIR dataFederated learningLung cancerMachine learningPrediction modelingSurvival analysis

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Area of Science:

  • Medical Informatics
  • Oncology
  • Data Science

Background:

  • Healthcare data sharing is crucial for scientific advancement but hindered by privacy and regulatory challenges.
  • The Personal Health Train (PHT) offers a privacy-by-design solution for connecting FAIR data sources.
  • PHT enables distributed data analysis and machine learning without compromising patient data privacy.

Purpose of the Study:

  • To evaluate the Personal Health Train (PHT) infrastructure for distributed analysis of lung cancer patient data.
  • To assess the feasibility of training machine learning models on multi-institutional, privacy-preserved healthcare data.
  • To demonstrate the PHT's capability in overcoming data sharing barriers across different regulatory environments.

Main Methods:

  • Lung cancer patient databases were standardized to a FAIR data model and stored locally.
  • Distributed machine learning algorithms were deployed via a central server, exchanging only summary statistics.
  • A logistic regression model was developed to predict two-year post-treatment survival, with evaluation using ROC curves and RMSE.

Main Results:

  • The PHT connected 8 healthcare institutes across 5 countries, integrating data from 23,203 lung cancer cases within 4 months.
  • A distributed logistic regression model was trained on 14,810 patients and validated on 8,393 patients.
  • The study successfully demonstrated the PHT's ability to perform cross-institutional data analysis.

Conclusions:

  • The PHT infrastructure effectively addresses patient privacy concerns in healthcare data sharing.
  • The PHT facilitates rapid, multi-institutional data analysis, promoting global evidence-based medicine.
  • This approach prioritizes patient privacy while enabling significant scientific insights from distributed healthcare data.