A methodology based on Trace-based clustering for patient phenotyping

摘要

Background:The current situation of critical progression as regards the resistance of bacteria to antibiotics has led to the use of machine learning techniques in order to provide clinicians with new knowledge for decision making. One of the key aspects is precision medicine, which focuses on finding phenotypes of patients for whom treatments may be more effective or detecting high risk patients whose progress must be closely monitored. The identification of these phenotypes requires the application of a methodology whose results are consistent and interpretable, along with the control of the process by a clinical expert. Studies concerning machine learning phenotyping use conventional clustering or subgroup algorithms that require information to be obtained a priori.