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Advancing Outcomes-Based AI to Interact with Trial Data

Only two months into 2019, Saama is excited to announce a capabilities expansion for its Deep Learning Intelligent Assistant (DaLIA). DaLIA, a context and domain-aware conversational user interface for Saama’s Life Science Analytics Cloud (LSAC), was launched in 2018 and shifted the human-computer interaction paradigm.

DaLIA’s new, heightened functionality reinforces Saama’s position as an industry leader in the outcomes-based application of AI. DaLIA’s capacity has been broadened to identify the clinical development-related intents (what you would like to do or know) of a query, catapulting the virtual assistant to an enhanced level of conversational user engagement.

When DaLIA replies to a researcher’s question about study conduct, whether from an operational or clinical perspective, it is now enabled to factor in key parameters, such as the names of persons, organizations, and locations, as well as expressions of times, quantities, monetary values and percentages. DaLIA remembers the context of previous inquiries and can seamlessly enfold new entities into the discussion to provide rapid clinical operations insights. Queries about various aspects of clinical development, including start-up, enrollment, data quality and financial risk, result in responses that factor in the intent and specificity of the questions. This allows DaLIA to mine the data resources from an enterprise’s LSAC deployment and provide answers.

DaLIA puts users in touch with deep learning/machine learning-augmented functions within LSAC that address the study planning, startup and conduct challenges of the life sciences industry and improves the industry’s ability to deliver safe and effective therapies. By using Natural Language Understanding (NLU), DaLIA creates a user interaction that is specifically framed by the particular subject area of clinical development.

DaLIA enables researchers to gain rapid insights into obstacles historically associated with clinical development. That’s vital since the life sciences industry often encounters difficulty achieving critically important clinical trial milestones, and there is a huge gap between desired outcomes and actual execution.

According to Pharmaceutical Processing’s 2016 ClinOps Benchmark Report, the majority of global leaders in clinical operations rank patient recruitment (95 percent), site productivity (75 percent), and patient compliance (65 percent) as very important, but they are unable to successfully achieve these milestones. Only 47 percent report successful enrollment, 22 percent say their site is productive, and 25 percent consider patient compliance efforts successful.

DaLIA would enable researchers an unprecedented conversational experience with their clinical trial data and rapid insights that can help overcome these types of challenges, embodying Saama’s commitment to the strategic use of outcomes-based AI that focuses on safer, more effective drug development. Saama is continuing to evolve DaLIA beyond information retrieval to eventually accommodate additional types of information requests.

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About Malaikannan Sankarasubbu

mmMalaikannan Sankarasubbu loves anything data and even more if it is un-structured. He has a keen interest in developing high performance Artificial Intelligence based GPU driven solutions for critical problems. With multiple top 10% finishes, he ranks in the top 1000 among 500,000 of the competitive community at Kaggle. He was earlier Founder and CTO of Datalog.ai a valley startup that focussed on Natural Language Understanding and Chatbots.


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