A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support

Probabilistic assessments of clinical care are essential for quality care. Yet, machine learning, which supports this care process has been limited to categorical results. To maximize its usefulness, it is important to find novel approaches that calibrate the ML output with a likelihood scale.