How is Machine learning Revolutionizing Pharma?
The advent of artificial intelligence, machine learning, and various other disruptive innovations has help pharma companies to deal with data analytics.
Fremont, CA: The pharmaceutical data analytics has been a manual and tedious task conducted by commercial research, health outcomes, R&D, and Clinical Study groups at Pharma companies, small and large. With the introduction of machine learning, artificial intelligence, and other disruptive innovations, Pharma, similar to 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.
Here are some use cases of machine learning and artificial intelligence in Pharma.
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Patient Finder using Claims Databases
Finding patients in claims databases can be accomplished by identifying patients who show similar characteristics to other patients with the same diagnosis codes. For instance, using a cohort of patients who have been confirmed to have diabetes, companies can create ML models, which can then be applied to other patients in order to identify potential undiagnosed cases or patients.
A standard patient identification approach can also be utilized to find patients with rare diseases.
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