Automated detection of adverse drug events from older inpatient's electronic medical records using structured data mining and natural language processing

Our research hypothesis is that the automated detection of ADEs from EMRs using structured data mining and natural language processing (SDM and NLP) could significantly improve risk management and patient safety in hospitalized older inpatients with multimorbidity, frailty and polypharmacy. It could additionally provide reliable data on incidence of ADEs for health care professionals, patient safety organisations and policy-makers.

The main objective of the project is to develop and validate an electronic application for the automated detection of ADEs related to antithrombotics based on structured data and free text mining.