Similarity-Principle-Based Machine Learning Method for Clinical Trials and Beyond

Abstract

With recent success in supervised learning, artificial intelligence (AI) and machine learning (ML) can play a vital role in precision medicine. Deep learning neural networks have been used in drug discovery when larger data is available. However, applications of machine learning in clinical trials with small sample size (around a few hundreds) are limited. We propose a Similarity-Principle-Based Machine Learning (SBML) method, which is applicable for small and large sample size problems. In SBML, the attribute-scaling factors are introduced to objectively determine the relative importance of each attribute (predictor). The gradient method is used in learning (training), that is, updating the attribute-scaling factors. We evaluate SBML when the sample size is small and investigate the effects of tuning parameters. Simulations show that SBML achieves better predictions in terms of mean squared errors for various complicated nonlinear situations than full linear models, optimal and ridge regressions, mixed effect models, support vector machine and decision tree methods.

Keywords:

• Artificial intelligence
• Clinical trials
• Decision tree methods
• Machine learning
• Ridge regression

Acknowledgments

The authors thank Robert Lew, Ph.D., Bingming Yi, Ph.D. and Michael LaValley, Ph.D. for their encouragement, support, constructive discussions and valuable comments.

Funding

The author(s) reported there is no funding associated with the work featured in this article.