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Found 4 papers, 1 papers with code
Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach
This renders the features amenable to train a variety of classifiers to perform causal inference.
A New Approach for Interpretability and Reliability in Clinical Risk Prediction: Acute Coronary Syndrome Scenario
In order to achieve the mentioned goals, a three-step methodology was developed: several rules were created by dichotomizing risk factors; such rules were trained with a machine learning classifier to predict the acceptance degree of each rule (the probability that the rule is correct) for each patient; that information was combined and used to compute the risk of mortality and the reliability of such prediction.
Personalized and Reliable Decision Sets: Enhancing Interpretability in Clinical Decision Support Systems
In this study, we present a novel clinical decision support system and discuss its interpretability-related properties.
Improving the compromise between accuracy, interpretability and personalization of rule-based machine learning in medical problems
Some procedures consider a simplification of ETs, using heuristic approaches to select an optimal reduced set of decision rules.
