machine-learning

Mental Health and Big Data: A Step in the Right Direction

 

 

 

 

 

 

Kate Spade – depression and anxiety

Robin Williams – severe depression

Pete Davidson – Borderline Personality Disorder

Demi Lovato – Bipolar disorder

Two gone, two battling a lifelong battle, like many Americans. With the stigma and bias surrounding the topic, the mental health community struggles to move forward. The research and treatment is at least a decade behind common chronic conditions, mainly because no one wants to talk about mental health due to the complexity and lack of understanding. 

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Machine Learning for Pharma using Random Forest, Part II

Introduction to Machine Learning with Random Forest (Pharma/Genetics)

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Machine Learning with CMS Public Healthcare Dataset, Part I

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 Rx Data Science.  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:

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Top Use Cases for Machine Learning in Pharma


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.

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