Search Results for author:
Found 4 papers, 0 papers with code
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.
Classification of Electrical Impedance Tomography Data Using Machine Learning
In this article, we present a new approach to promote the use of EIT data and the implementation of new clinical applications for differential diagnosis, with the development of several machine learning models to discriminate between EIT data from healthy and non-healthy subjects.
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.
An open access database for the evaluation of respiratory sound classification algorithms
###Objective: Over the last few decades, there has been significant interest in the automatic analysis of respiratory sounds.
