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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

2022-12-06

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Goal</h4> 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 in real-world settings, remain significant constraints to their widespread adoption in clinical practice. One approach...

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Literature Corpus work: bd76cec7-544e-57f2-9824-0d445daeb5bc
DOI: 10.1101/2022.12.02.22283033

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