Build a Chatbot for Clinical Trials Across Multiple Data Sources

Unify the access to a knowledge graph, a vector database and the web in a LangChain chatbot

Clinical research plays a crucial role in advancing medical knowledge, improving patient care, and developing innovative treatments and therapies. It evaluates the safety and efficacy of medical interventions, including pharmaceuticals, medical devices, and behavioral interventions among human participants. Clinical research metadata is systematically uploaded to the widely recognized web portal, clinicaltrials.gov. This invaluable platform serves as a hub for medical professionals, enabling them to stay abreast of the latest advancements and developments within the pharmaceutical industry.

Figure 1. The architecture of the project Clinical Trials as Graphs and Vectors. Image by author.

However, navigating and searching through clinicaltrials.gov can be challenging and cumbersome. In order to enhance user experience, I undertook a project called Clinical Trials as Graphs and Vectors, where I restructured data from clinicaltrials.gov and SNOMED into a Neo4j knowledge graph and a Qdrant vector database (Figure 1). Users can analyze the graph with Cypher and search the vector database semantically. With these two databases, users can quickly see who are the competitors, how they design the trials, and what the results are.