RxDataScience Blog

Background

From the outset, the term “Machine Learning” can seem very daunting to those unfamiliar with the technicalities of what this actually means, or so it seemed to me when I was initially assigned to develop a use case for one of these algorithms during my time here at RxDataScience.  From a quick study into the topic, Machine Learning (ML), put simply, is a branch of Artificial Intelligence (AI) that allows a system to automatically learn and improve itself without being explicitly instructed to, by using past and present data to predict certain outcomes [1].  The following video provides a gentle introduction into what ML is all about:

One life gone. Now, another. By the end of today, the Centers for Disease Control and Prevention predict that, on average, 115 Americans will die from an opioid overdose. The cycle will repeat tomorrow and the next day as well as the next.  

What was once a chilling feeling has turned numb, leaving the public unphased by the steadily increasing body count scattered across the headlines.

Real-World Use Cases for AI & ML in Pharma

For decades, Pharmaceutical data analytics has been a largely manual and tedious task conducted by the commercial research, health outcomes, R&D and Clinical Study groups at Pharma companies both small and large. With the emergence of machine learning, artificial intelligence and other disruptive innovations, Pharma, like other industries has also started its slow but sure transition to a more agile, data-driven model – one where in-house research is supplemented by intelligence gathered by applying algorithms across terabytes of Physician Rx, Patient Claims and other related datasets.