pharmaceuticals

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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Hope on the Horizon: How data can showcase the bigger picture of the opioid crisis

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.

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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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