This article introduces using the langchain framework supported by IRIS for implementing a Q&A chatbot, focusing on Retrieval Augmented Generation (RAG). It explores how IRIS Vector Search within langchain-iris facilitates storage, retrieval, and semantic search of data, enabling precise and up-to-date responses to user queries. Through seamless integration and processes like indexing and retrieval/generation, RAG applications powered by IRIS enable the capabilities of GenAI systems for InterSystems developers.
InterSystems FHIR SQL Builder is a powerful tool to create analytics-ready SQL projections of FHIR data. One analytics use case that many practitioners want to explore is developing predictive models for clinically relevant events, such as disease risk based on historical data. IntegratedML, another powerful tool from InterSystems, is a natural fit for the output of FHIR SQL Builder, and many customers are asking if they can combine them. The answer is yes, in principle. However, real-world healthcare data, which can be complicated and messy, will likely need transformation and cleaning before meaningful machine learning models can be generated. We'll show you how to use "modern data stack" tooling, such as dbt, to bridge the gap between raw FHIR data and IntegratedML predictive models.
ChatIRIS Health Coach, a GPT-4 based agent that leverages the Health Belief Model (Hochbaum, Rosenstock, & Kegels, 1952) as a psychological framework to craft empathetic replies.
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