Using machine learning to identify adverse events from scientific literature

Presented by

Umesh Nandal

About this talk

Information found in the biomedical literature is a significant source for tracking and reporting adverse drug reactions (ADR). The EMA and FDA have both mandated that market authorization holders maintain active screening of literature for any mentions of ADRs related to their drugs or other medicinal products. Given the increasing amount of literature, manual screening, reviewing and monitoring literature costs more time, money and creates an additional compliance risk. Using the advanced technologies in Artificial intelligence (AI), Machine learning (ML) and Natural language processing (NLP), we have developed models to identify Adverse events (AE) in the literature, which can save considerable time and effort in large-scale analysis and in integrating data from multiple diverse information sources. This webinar will discuss: - the challenges of literature mining using AI - the Biomedical Named Entity Recognition (BNER) and its advantages for information extraction tasks - how to create a quality training set for machine learning - the experiment outcome and further applications About speaker: Umesh Nandal, PhD, is the Principal Machine Learning & NLP scientist in Content Transformation (CT) department at Elsevier. With a background in Chemistry and computational biology, Umesh is applying state-of-the-art methods in ML and NLP to improve or build new life science products of Elsevier that can help researchers in getting correct answers to their questions quickly. Prior to joining Elsevier, he used various ML and computational approaches to analyse molecular data generated from high-throughput technologies to understand biological processes in healthy and diseased organisms. During his PhD, he intensively worked on the comparison of mouse models with humans by building a network based integration method that can compare their biological networks.

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