Computational Simulation of Virtual Patients Reduces Dataset Bias and Improves Machine Learning-Based Detection of ARDS from Noisy Heterogeneous ICU Datasets

<italic>Goal:</italic> Machine learning (ML) technologies that leverage large-scale patient data are promising tools predicting disease evolution in individual patients. However, the limited generalizability of ML models developed on single-center datasets, and their unproven performance...

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Bibliographic Details
Main Authors:	Konstantin Sharafutdinov, Sebastian Johannes Fritsch, Mina Iravani, Pejman Farhadi Ghalati, Sina Saffaran, Declan G. Bates, Jonathan G. Hardman, Richard Polzin, Hannah Mayer, Gernot Marx, Johannes Bickenbach, Andreas Schuppert
Format:	Article
Language:	English
Published:	IEEE 2024-01-01
Series:	IEEE Open Journal of Engineering in Medicine and Biology
Subjects:	ARDS computational simulation dataset bias machine learning virtual patients
Online Access:	https://ieeexplore.ieee.org/document/10040737/
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Computational Simulation of Virtual Patients Reduces Dataset Bias and Improves Machine Learning-Based Detection of ARDS from Noisy Heterogeneous ICU Datasets

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