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Computational Simulation of Virtual Patients Reduces Dataset Bias and Improves Machine Learning-Based Detection of
Konstantin Sharafutdinov1,2,3, Sebastian Johannes Fritsch3,4,5, Mina Iravani1,2,3
1Institute for Computational BiomedicineRWTH Aachen University 52062 Aachen Germany.
IEEE Open Journal of Engineering in Medicine and Biology
|August 26, 2024
Summary
Mechanistic virtual patient (VP) modeling enhances machine learning by reducing bias in multi-center data, improving disease prediction and patient cohort discovery for conditions like acute respiratory distress syndrome (ARDS).
Area of Science:
- Computational biology
- Medical informatics
- Machine learning
Background:
- Machine learning (ML) models show promise for predicting disease progression but struggle with generalizability due to single-center data limitations.
- Multi-center datasets improve ML generalizability but introduce biases from data heterogeneity across institutions.
Purpose of the Study:
- To demonstrate how mechanistic virtual patient (VP) modeling can mitigate biases in heterogeneous multi-center datasets.
- To improve the robustness and clinical applicability of ML models in healthcare.
Main Methods:
- Utilized mechanistic VP modeling to capture patient-specific dynamics and reduce dataset biases.
- Applied VP modeling for data augmentation by individualizing model parameters for patients with suspected acute respiratory distress syndrome (ARDS).
- Compared unsupervised learning (clustering) on original versus VP model-derived data.
Main Results:
- VP model-derived data yielded more robust clustering configurations compared to original patient data.
- VP model-based clustering effectively reduced biases from multi-center data origins.
- An additional patient cluster with significant ARDS enrichment was discovered using the VP model approach.
Conclusions:
- Mechanistic VP modeling significantly reduces biases inherent in heterogeneous datasets for ML.
- VP modeling enables improved discovery of patient cohorts based purely on medical conditions, enhancing clinical relevance.
