Personalized prognosis & treatment using Bayesian nonparametric inference: An example study on conversion from Mild Cognitive Impairment to Alzheimer's Disease

The present work presents a statistically sound, rigorous, and model-free inference method for use in personalized medicine, together with a software implementation. The algorithm is designed first to learn from a set of clinical data with relevant predictors and predictands, and then to assist a clinician in the assessment of prognosis & treatment for new patients. It allows the clinician to input, for each new patient, additional patient-dependent clinical information, as well as patient-dependent information about benefits and drawbacks of available treatments. For this reason we call it an "optimal predictor machine". We apply this method and software in a realistic setting for clinical decision-making, incorporating clinical, environmental, imaging, and genetic data, using a data set of subjects suffering from mild cognitive impairment and Alzheimer’s Disease. We show how the algorithm is theoretically optimal, and discuss some of its major advantages for decision-making under risk, resource planning, imputation of missing values, assessing the prognostic importance of each predictor, and further uses.