Machine learning and Visualization techniques to extract knowledge from electronic health records

The generalization and implementation of the electronic medical record (EHR) has generated large amounts of clinical data. The EHR of a patient is the longitudinal record of all events related to a person’s health, both preventive and care, and that contains clinical notes, diagnosis codes, medications, laboratory tests or vital signs, among others. As a result, the EHR contains valuable information on clinical observations that describe both the health of patients and the care provided by clinical experts. However, the heterogeneous nature of this information is complex to analyze. Therefore, the objective of this talk is to deepen the design and adaptation of machine learning and visualization methods to discover clinically relevant patterns from data collected in the electronic medical record.