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Sample Size Analysis for Machine Learning Clinical Validation Studies.

Daniel M Goldenholz1,2, Haoqi Sun1,2,3, Wolfgang Ganglberger1,2,3

  • 1Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.

Biomedicines
|March 29, 2023
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Summary
This summary is machine-generated.

A new open-source tool, Sample Size Analysis for Machine Learning (SSAML), helps determine necessary sample sizes for validating clinical machine learning (ML) models. This method ensures desired precision and accuracy for model performance estimates.

Keywords:
machine learningpower calculationstatistics

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Area of Science:

  • Clinical validation of machine learning (ML) algorithms.
  • Statistical methods for predictive modeling.

Background:

  • Clinical integration of ML models necessitates rigorous validation.
  • Accurate sample size estimation is crucial for validating predictive model performance.
  • A standardized method for sample size calculation in clinical ML validation is lacking.

Purpose of the Study:

  • To introduce and evaluate an open-source method for sample size estimation in clinical ML validation.
  • To provide a formal expectation of precision and accuracy for ML model performance.

Main Methods:

  • The Sample Size Analysis for Machine Learning (SSAML) method was developed.
  • SSAML was tested on three diverse ML models: Cox Proportional Hazard for brain age and mortality, ordinal regression for COVID hospitalization risk, and deep learning for seizure forecasting.

Main Results:

  • Minimum sample sizes were successfully determined for each validation dataset.
  • The method established standardized criteria for sample size calculation.

Conclusions:

  • SSAML offers a formal approach to achieving desired confidence levels for ML model precision and accuracy.
  • The SSAML tool is open-source, data-type agnostic, and model-agnostic, making it broadly applicable for clinical ML validation studies.