Session
Harnessing Databricks for Advanced LLM Time-Series Models in Healthcare Forecasting
Overview
Experience | In Person |
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Type | Lightning Talk |
Track | Artificial Intelligence |
Industry | Health and Life Sciences |
Technologies | Delta Lake, MLFlow, Mosaic AI |
Skill Level | Intermediate |
Duration | 20 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