Drug Safety Intelligence and Automation

A huge amount of data about adverse drug reactions are in medical cases, and that is a major challenge for experts to generate reports and analyze it. Time and money are wasted in finding efficient strategies for identifying and extracting this type of information. Our paper focuses on solving problems in report generation of free text cases and reports to support Pharmacovigilance research and decision-making. Hence, Machine learning with the help of NLP is used in the identification of MEDLINE case. An automated system to solve the problem is developed to identify drug reactions and effects from ICSRs (Individual Case Safety Report) and drug safety classification of adverse drug events from free-text electronic patient records and information. A further implementation into an AI-based mass casualty management framework is considered for future work to aid the decision-making right after triage.