The fused lasso penalty for learning interpretable medical scoring systems

Score learning aims at taking advantage of supervised learning to estimate interpretable models which facilitate decision making. Ideally, a scoring system is based on simple arithmetic operations, is sparse, and can be easily explained by human experts. In this contribution, we introduce an original methodology to simultaneously learn interpretable binning mapped to a class variable, and the weights associated with these bins contributing to the score. We show by numerical experiments on benchmark data sets that our approach is competitive compared to the state-of-the-art methods. We illustrate by a real medical problem of type 2 diabetes remission prediction that a scoring system learned automatically is comparable to one manually constructed by clinicians.