Title: COTCAgent: Preventive Care Proactive Consultation Driven by a Probabilistic Chain-of-Thought Completion Framework
Primary area: interpretability and explainable AI
Keywords: Chain-of-Thought，LLM Agent，Healthcare，Time Series Indicators，Risk Quantification

Abstract: Current agent-based healthcare AI systems struggle with dynamic temporal reasoning and hallucination mitigation, limiting their preventive care utility. We introduce COTCAgent, a proactive consultation framework featuring a novel Probabilistic Chain-of-Thought completion mechanism. Our approach integrates two synergistic modules: a Time Series Analysis Module that extracts clinical trends from longitudinal EHRs, and a COTC Module that calculates disease risks via Inverse Disease Frequency weighting and completes reasoning chains through targeted questioning. Extensive evaluations demonstrate state-of-the-art performance, with COTCAgent achieving 89.2\% accuracy in medical risk prediction (vs. 77.9-80.2\% for baselines) and 69.8\% on the challenging HealthBench sequential diagnosis benchmark. This work bridges temporal analysis with probabilistic reasoning to enable truly personalized preventive care.

TL;DR: The first to propose a query method for completing the Chain-of-Thought (CoT) based on time-series health check-up data, which significantly improves the accuracy and satisfaction of personalized proactive consultation and risk prediction