Ascent: An Agentic System over the Model Context Protocol for Real-World Clinical Data Analysis
cs.AI, cs.IR
Submitted: 2026-09-21
Updated: 2026-09-21
Code: https://github.com/bayer-group/ascent-epi-demo
License: http://creativecommons.org/licenses/by/4.0/
The gist: Answering epidemiological questions from real-world clinical data requires medical coding, schema-aware SQL, and validation of implicit choices about populations, denominators, and time.
Terminology
Abstract
Answering epidemiological questions from real-world clinical data requires medical coding, schema-aware SQL, and validation of implicit choices about populations, denominators, and time. We present Ascent, an agentic system that exposes medical coding, question answering, and cohort analysis through a shared Model Context Protocol tool surface for standardized and native schemas. We introduce EpiTrap, a dataset testing whether systems avoid recognized pharmacoepidemiological errors, and compare a fixed pipeline with agents across models and orchestrators. With capable models, agents improve accuracy over the fixed pipeline by an average of 27 and 20 percentage points on native and standardized schemas, respectively. These gains require more tool calls and longer runtimes. Experience from real projects highlights the system's value for feasibility assessment, diagnostic iteration, and expert-guided analysis.
Sources
- DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
- A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
- EHR-MCP: Real-world Evaluation of Clinical Information Retrieval by Large Language Models via Model Context Protocol
- CodeClinic: Evaluating Automation of Coding Skills for Clinical Reasoning Agents
- gpt-oss-120b & gpt-oss-20b Model Card
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