Analyzing possible drug safety incidents and generating narratives in pharmacovigilance process have traditionally relied upon manual review of case reports from patients, consumers and healthcare professionals. However, due to the vast quantity and complexity of data to be analyzed and for ensuring timeliness, reduction of cost, consistency of reporting and quality of reporting; role of automated computational systems that can accurately detect adverse drug reactions attached to a suspected drug in a timely fashion have become critical. Pharmaceutical companies have started to realize the need for collaborative and integrative approaches and strategies to allow a faster identification of high-risk interactions between marketed drugs and adverse events, and to enable the automated uncovering of scientific evidence behind them. The fundamental requirement for the automatic processing of biomedical text is the identification of information carrying units such as the concepts or named entities. Additionally, there are regulatory guidance or rules with respect to identifiability of reporters, patients, drugs and interactions in the reports of suspected adverse reactions. Owing to these challenges, the problems of automated unambiguous identification of medical drugs and compounds, detection of adverse drug reactions, and generation of case narratives from the text of the reports are not considered to be adequately solved so far. In this paper, we present a novel text analysis platform that assists in bringing intelligent automation in the process by integrating a medical language processing pipeline and causal reasoning chain, with publicly available large-scale biomedical databases containing structure, bioassay, and genomic information, as well as comprehensive clinical data sets.
