Session

Harnessing Databricks for Advanced LLM Time-Series Models in Healthcare Forecasting

Overview

ExperienceIn Person
TypeLightning Talk
TrackArtificial Intelligence
IndustryHealth and Life Sciences
TechnologiesDelta Lake, MLFlow, Mosaic AI
Skill LevelIntermediate
Duration20 min
This research introduces a groundbreaking method for healthcare time-series forecasting using a Large Language Model (LLM) foundation model. By leveraging a comprehensive dataset of over 50 million IQVIA time-series trends, which includes data on procedure demands, sales, and prescriptions (TRx), alongside publicly available data spanning two decades, the model aims to significantly enhance predictive accuracy in various healthcare applications. The model's transformer-based architecture incorporates self-attention mechanisms to effectively capture complex temporal dependencies within historical time-series trends, offering a sophisticated approach to understanding patterns, trends, and cyclical variations.

Session Speakers

IMAGE COMING SOON

Yunlong Wang

/AI Scientist Dir
IQVIA