Randomness from model optimization and initialization causes individual risk predictions in flexible ML models to vary as much as resampling the full training data, potentially flipping clinical decisions near thresholds.
Springer, 2009
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
A rank reduction autoencoder combined with classification predicts numerical dispersion in automotive crash simulations more effectively than random forests when using wavelet or slope signal inputs.
A modular Quantum Sequential Model is proposed and compared against classical regression and symmetry-constrained quantum regressors for predicting hydration status from urinary biomarkers, highlighting opportunities and limitations of near-term quantum computing in digital health.
citing papers explorer
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Diagnostics for Individual-Level Prediction Instability in Machine Learning for Healthcare
Randomness from model optimization and initialization causes individual risk predictions in flexible ML models to vary as much as resampling the full training data, potentially flipping clinical decisions near thresholds.
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CRADIPOR: Crash Dispersion Predictor
A rank reduction autoencoder combined with classification predicts numerical dispersion in automotive crash simulations more effectively than random forests when using wavelet or slope signal inputs.
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Hydration Monitoring Using Urinary Biomarkers: A Hybrid Classical Quantum Predictive Modeling Framework
A modular Quantum Sequential Model is proposed and compared against classical regression and symmetry-constrained quantum regressors for predicting hydration status from urinary biomarkers, highlighting opportunities and limitations of near-term quantum computing in digital health.