We are very happy that Sherri Matis-Mitchell gave an invited talk in BioASQ workshop on August 13.
Title: "Solving Problems and Supporting Decisions in Pharma R&D using Text Analytics: A Recent History"
Abstract: Drug discovery in Pharma R&D is an information driven process requiring many disparate bits of data from many different sources, both structured and unstructured. Text mining is the key methodology used to extract entities and relationships from unstructured text in the quest for the knowledge needed to bring a safe and effective drug to market and beyond. Much of the insight needed in early drug research to identify drug target to disease relationships and progress a potential drug target, comes from published literature and internal reports. Later stage drug development requires many additional sources of information including case reports, clinical trials, competitive intelligence and other diverse sources. Following launch of a medicine to market, safety surveillance is needed to ensure safety and efficacy of medical products and devices. All pharmaceutical companies have a benevolent focus on improving patient lives so improving the information flow with patients while keeping in mind patient benefit, regulatory concerns and maintain privacy, should naturally follow.
Still there is unmet need. When a patient receives a life altering disease diagnosis and subsequent treatment, for diseases like diabetes or cancer, many will turn to social media as a means of support and to find additional information to supplement what their provider has given them. There are small communities of patients with Rare diseases that desperately need treatment an many of these communities are active on social media and are empowered to: first, influence Pharma to listen and help by providing new treatments and second, to influence the regulatory agencies to offer incentives to develop treatments. Finally, when unwanted or adverse events do occur in the course of drug development and especially after launch, we need to detect all of the adverse events published in the literature and other non structured text, like reports.
In this presentation, I will provide an overview and present some examples of how text mining can be used in Pharma R&D to identify information to support drug development and improve the patient journey.
Biosketch: Sherri Matis-Mitchell PhD is currently a independent consultant for Text, Data and Social Media Analytics at Data Star Insights, serving the pharmaceutical and other life sciences industries. She is a senior informatics professional with experience in informatics, including data leadership roles at AstraZeneca and Elsevier. She has a wide breadth of expertise in text analytics, knowledge engineering, data science, natural language processing, genomics and preclinical, translational, and clinical Informatics. Sherri received her PhD in Molecular Biology from the University of Pittsburgh, School of Medicine and did her post- doctoral work in Informatics at Oak Ridge National Laboratory where she worked on feature and gene prediction in the human and bacterial genomes. She has received a number of awards for excellence and innovation, and has numerous articles in peer reviewed publications. Sherri is an industry-recognized speaker on the use text-mining of social media and developing analytics to support R&D decision making.
