From Pain to Strain: Using Machine Learning for Efficacy - Masterclass

In the Medical Cannabis ecosystem, one of the primary pain points for cultivators and consumers alike is compliance, authentication, tracking, and certification of products to ensure quality, consistency, and, most importantly, efficacy.

Cannabis as a plant is highly variant by nature; as such each gram’s lifecycle from cultivation to consumption needs to be attested for. Thousands of these resultant data sets provide clear evidence of “what happens to someone at a given point in time when a specific cannabis product is used to treat a specific symptom under a set of circumstances”.

In his MasterClass, Brad Moore, GCAC CEO, will discuss how GCAC’s Citizen Green Efixii harnesses the first-of-its-kind end-to-end solution, based on multiple interlocking technologies such as mobile applications, artificial intelligence, smart databases and an Ethereum blockchain.

This big data processed by the CitizenGreen ‘Pain to Strain’ machine learning algorithm can generate ‘averaged’ efficacy ratings on a per product-treatment basis. GCAC’s CitizenGreen Efixii technology provides the medical cannabis data that cultivators need in order to provide the best products. Regulators need to write meaningful regulations. Doctors need to write correct prescriptions, and patients need to confidently use products.