arXivPaperNeeds Review
DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening
DIASENTINEL combines deterministic extraction, guideline retrieval, risk prediction, and hybrid verification on-premise. It is a useful architecture reference for auditable agents handling sensitive data.
arXiv
Source Summary
DIASENTINEL is an **on-premise multi-agent system** for one-year diabetes-risk screening from EHRs. It combines calibrated prediction, deterministic signal extraction, **Reciprocal Rank Fusion** over ADA guidelines, and rule-plus-LLM verification.
Practical Implication
For high-stakes agent workflows, separate extraction, retrieval, generation, and verification instead of asking one model to do everything. Preserve cited recommendations, verification outcomes, and raw-input comparisons for review.
Agent-Ready Context
DIASENTINEL is an **on-premise multi-agent system** for one-year diabetes-risk screening from EHRs. It combines calibrated prediction, deterministic signal extraction, **Reciprocal Rank Fusion** over ADA guidelines, and rule-plus-LLM verification. For high-stakes agent workflows, separate extraction, retrieval, generation, and verification instead of asking one model to do everything. Preserve cited recommendations, verification outcomes, and raw-input comparisons for review. The material describes a demonstration but gives no accuracy, calibration, hallucination, or operational benchmarks. Its reliability and clinical usefulness therefore cannot be judged from the abstract alone.
Context Map
agentdata#multi-agent#retrieval#agent-reliabilityUncertainty
The material describes a demonstration but gives no accuracy, calibration, hallucination, or operational benchmarks. Its reliability and clinical usefulness therefore cannot be judged from the abstract alone.