Reproducibility in machine learning for health research: Still a ways to go
Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Luca Foschini, Marzyeh Ghassemi
Abstract
Machine learning for health must be reproducible to ensure reliable clinical use. We evaluated 511 scientific papers across several machine learning subfields and found that machine learning for health compared poorly to other areas regarding reproducibility metrics, such as dataset and code accessibility. We propose recommendations to address this problem.
Topics & Concepts
Machine learningComputer scienceReproducibilityArtificial intelligenceData scienceMathematicsStatisticsExplainable Artificial Intelligence (XAI)Machine Learning in HealthcareScientific Computing and Data Management