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The Large Scale Visual Recognition Challenge based on the well-known Imagenetdataset catalyzed an intense flurry of progress in computer vision. Benchmarktasks have propelled other sub-fields of machine learning forward at an equallyimpressive pace, but in healthcare it has primarily been image processingtasks, such as in dermatology and radiology, that have experienced similarbenchmark-driven progress. In the present study, we performed a comprehensivereview of benchmarks in medical machine learning for structured data,identifying one based on the Medical Information Mart for Intensive Care(MIMIC-III) that allows the first direct comparison of predictive performanceand thus the evaluation of progress on four clinical prediction tasks:mortality, length of stay, phenotyping, and patient decompensation. We findthat little meaningful progress has been made over a 3 year period on thesetasks, despite significant community engagement. Through our meta-analysis, wefind that the performance of deep recurrent models is only superior to logisticregression on certain tasks. We conclude with a synthesis of these results,possible explanations, and a list of desirable qualities for future benchmarksin medical machine learning.

Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data