Ascent: An Agentic System over the Model Context Protocol for Real-World Clinical Data Analysis

2026-09-21

AI Summary

本文介绍Ascent系统，通过模型上下文协议解决真实临床数据分析中的流行病学问题，使用EpiTrap数据集测试显示其准确性优于固定管道。

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.

Problem

• real-world clinical data
• epidemiological questions
• medical coding
• schema-aware SQL
• cohort analysis

Innovation

• Agentic System
• Model Context Protocol
• EpiTrap
• Pharmacoepidemiological Errors