The client is one of the largest pharmaceutical companies in the world and spends hundreds of millions of dollars in clinical trials to formulate newer drugs. Clinical studies and trials involve large volumes of structured and unstructured data which require analytics for deriving meaningful insights. Nagarro developed a comprehensive big data and machine learning based cognitive computing framework which leveraged the client data and guided a learning system to process information from data and generate insights.
During the clinical trial cycles, enormous amount of data is generated at each stage. The pharma leader wanted to leverage technology and cognitive computing to analyze the large volumes of data, help generate insights regarding rationale for failed clinical studies, and leverage that to monitor studies going forward. Analytical insights will allow them to predict failures early, take corrective action or terminate the studies and save costs.
Given the nuanced and ambiguous nature of this project, we ran a series of natural language processing and machine learning based functional and technical experiments (each ranging from a few hours to few days) and documented those results to understand opportunities, feasibility and limitations of what could be accomplished.
In this project, we leveraged ‘signal extraction framework’ to mine the right insights determining success or failure of clinical trial, from a large unstructured set of data. We employed a disease area specific clinical trial success framework to develop detailed compound profiles and interpret the signal from. Using the insights coming from the above exercise, we developed a dashboard wherein our client could look at the summary profile of each candidate being experimented in the clinical study or any competitor compounds.
