Graphing Patient Data with LLMs (e.g., ChatGPT)

Our invited guest speaker Dr. Jans Aasman is CEO of Franz Inc., a pioneering American company of Artificial Intelligence (AI) and a leading provider of semantic Graph Database and Neuro-Symbolic AI technologies that serves numeral client FORTUNE 500 companies as well as US and foreign government, to develop complex data fabric solutions across various sectors including healthcare and telecommunications. As a thought leader and innovation driver, Jans is a frequent speaker in the semantic technology industry whom shapes trends and advances in AI and Knowledge Graph. His background in telecommunications research, applied AI projects and intelligent user interfaces contributes to Franz's innovative approaches of developing flagship products such as AllegroGraph. He works directly with client organizations to ensure Franz's technologies meet real world needs and quality standards within the industry. Over his career, Jans has patented in the areas of speech technology, multimodal user interaction and recommendation engines (e.g., precursor technologies for iPad and personal assistants including Siri). He was a Member of CEO Alliance, Business Developer of TNO Telecom, Professor at the Industrial Design Engineering (IDE) Department of the Delft University of Technology, and Senior Scientist at KPN Research. Jans holds a PhD degree in Cognitive Science from University of Groningen, studying cognitive modeling of Traffic Science. 

Abstract: Larger hospitals and major payers favor Knowledge Graphs over relational databases primarily due to the complexity of patient data and life science terminology systems. Jans will present an approach of modeling that allows viewing patient both as a series of events, which is necessary for machine learning (ML) and predictive analytics, and as a 360-degree graph, which is essential for all kinds of graph analytics. A significant part of his presentation will be dedicated to clarify what is now called Neuro-Symbolic AI, as a combination of logic, ML and generative AI (GenAI), with examples demonstrating how they can be utilized in Knowledge Graph. He will also explain how large language models (LLMs) can improve the extract, transform and load (ETL) processes within Knowledge Graph, enhance semantic matching between vocabulary terms significantly, and create predictions based on event streams, as well as how to query Knowledge Graph using natural languages.

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