Predicting Repeat Admissions to Substance Abuse Treatment with Machine Learning
Description
In our presentation, we will walk through a model created to predict repeat admissions to substance abuse treatment centers. The goal is to predict early who will be at high risk for relapse so care can be tailored to put additional focus on these patients. We used the Treatment Episode Data Set (TEDS) Admissions data set, which includes every publicly funded substance abuse treatment admission in the US. While longitudinal data is not available in the data set, we were able to predict with 88% accuracy and an f-score of 0.85 which admissions were first or repeat admissions. Our solution used a scikit-learn Random Forest model and leveraged MLFlow to track model metrics to choose the most effective model. Our pipeline tested over 100 models of different types ranging from Gradient Boosted Trees to Deep Neural Networks in Tensorflow. To improve model interpretability, we used Shapley values to measure which variables were most important for predicting readmission. These model metrics along with other valuable data are visualized in an interactive Power BI dashboard designed to help practitioners understand who to focus on during treatment. We are in discussions with companies an…
Description from YouTube. Full content on the video page.
Topics
More from Databricks
ReleasesDatabricks launches across the Data + AI stack in 90 seconds
Databricks announced LTAP to unify lakebased and lakehouse data, eliminating ETL and enabling a single copy of data for analytical and operational needs. They also introduced Unity AI Gateway for governance, Genie Ontology for enterprise knowledge graphs, and open-sourced Omniant for managing multiple coding agents.
ReleasesIntroducing Omnigent: The Ultimate Meta-Harness for AI Agents
Omnigent is a new open-source meta-harness for AI agents that provides a unified interface for composition, control, and collaboration across multiple models and agent workflows. It enables stateful, data-centric policies for guardrails and allows real-time sharing and steering of live agent sessions with teammates.
NewsHow DEFRA and Natural England Accelerate Peatland Restoration with AI and Databricks
DEFRA and Natural England utilize AI and Databricks to accelerate peatland restoration by automating the mapping of peatland features and peat dams across England. This technology significantly reduces the time required for mapping, enabling faster identification and restoration of these crucial carbon-storing habitats.
NewsAI Stack Explained in 3 Layers (LLM, Agent Harness, Omnigent)
The AI stack now includes a third layer, the meta harness, which sits above individual agent harnesses. This meta harness, exemplified by Databricks' open-sourced Omnigent, allows for routing queries to appropriate agents and orchestrating tasks across multiple agents, enabling seamless interaction and context sharing between them.
NewsWhat’s coming next to Free Edition
Databricks announces the availability of Genie, GPUs, Agent Hooks, Lakehouse, and Lake Flow Designer on its Free Edition. This update provides virtually all of Databricks' production platform features for free, enabling users to learn and build data and AI projects.