Data2Life’s AI Powered RWE Platform Supports the CannaPharma Industry’s Next Big challenge


Recent FDA hearing about CBD products emphasises the need for solid Real-Word Evidence Data


TEL AVIV, Israel, June 10, 2019 /PRNewswire/ — Data2Life technology will facilitate the needs of the fast-growing CannaPharma industry—an industry set to be worth $37 billionby 2023 and currently in need of a technology enabler for Real-World Evidence (RWE) Practices which is becoming a requirement within this emerging field.


On December 6, 2018, the U.S. Food and Drug Administration (FDA) issued a framework guideline announcing, “a new strategic framework to advance the use of real-world evidence to support development of drugs and biologics.” While Real World Data may be found in, “a diverse array of sources, such as electronic health records (EHR), medical claims, product and disease registries, laboratory test results, and even cutting-edge technology paired with consumer mobile devices,” it is only by using this data to create RWE that the Pharma industry can be pushed forward.

On May 31, 2019 the FDA acting chief said, at the first public hearing on cannabis, that they have many unanswered questions and concerns about CBD products, suggesting that the FDA believes there is not enough solid data to support a decision about cannabis abilities or side effects on humans.

With this call to action to bring forth Real World Data, Limor BH Epstein, Co-Founder of Data2Life believes the company’s technology fills the integral gap in cannabis research. “By harmonizing all traditional clinical data along with patients’ online chatter, we are transforming unstructured Real-World Data into actionable Evidence. Cannabinoids medical research is the next expected regulatory precedent, and accessing insights from currently existing data will be the industry’s greatest challenge.”


Data2Life accelerates the Life Science Processes for the “Cannabis Going Pharma” Industry

Data2Life provides one holistic platform that aggregates patient-voice from online communities, medical literature, clinical records (EHR\EMR), internal or regulatory databases, public datasets, and data from third-party health apps. This source-agnostic automated data platform integrates all data formats and structures (both structured and unstructured data). The company’s Diagnostic NLP (Natural Language Processing), trained for understanding the complex health domain, is now considered the next generation of Medical NLP.

Businesses can benefit from a harmonized data connections and knowledge graph that cross-references and recognizes semantics to find patterns, and uses cutting edge Artificial Intelligence, including standard machine learning and deep-learning algorithms to recognize free text and uncover previously unseen trends.


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