In her article in Rx Data, Jane Reed, Director Life Science at Linguamatics, discusses the impact of advanced data technologies (artificial intelligence and machine learning) on innovation in drug discovery, development and delivery.
We are now in the fourth industrial revolution (4IR), known to some as the Big Data Revolution. Advances in connectivity and communication, in the digital revolution, bring results such as improved data access and the new-found potential to analyze huge volumes of data. The ability to access these important volumes of varied data and to connect, integrate, query and analyze it is enabling fundamental changes in how we envision drug discovery and delivery in the clinic. Additionally, the pace of these changes is also remarkable; Jane notes a few examples of some genome-based projects and the fast-paced evolution, from the first human chromosome sequenced in 1999; the human genome published in draft in 2001, to the more recent UK 100k Genome project.
According to Jane, the key components for these innovations include data integration and data analysis. To keep up with that rhythm, pharma companies now need to join up genomic data with clinical information and knowledge about particular diseases.
Since we now have more data that the human brain could decipher, we are turning to Artificial Intelligence (AI) technologies, such as Natural Language Processing (NLP), to apply algorithms and machine learning models to help search for patterns and insights from huge volumes of data. Within development (e.g., clinical, regulatory or safety), robotic process automation (RPA) can integrate into the daily lives of employees, reducing the burden of repetitive tasks, such as receiving, checking and filing case forms. NLP can assist in extracting key – sometimes hidden - data from unstructured text, and thus provide critical decision support to the pharma and healthcare industries.
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Learn more about NLP text mining and how pharma and healthcare organizations are getting ROI from AI-powered text mining to boost innovation, speed R&D and clinical processes, reduce risk, and improve patient outcomes.