标题： NeuroPal：一种结合睡眠昼夜疗法、认知行为重构和适应性植物化学干预的临床信息丰富的心理健康多功能大型语言模型助手 显示英文标题

标题： NeuroPal: A Clinically-Informed Multimodal LLM Assistant for Mental Health Combining Sleep Chronotherapy, Cognitive Behavioral Reframing, and Adaptive Phytochemical Intervention

摘要： 由于时间限制，中国的心理健康专业人士无法为患者提供长时间的谈话治疗，导致心理障碍患者的护理出现缺口，包括异常的睡眠和饮食模式、不适应的归因风格以及胃肠道功能紊乱等问题。 为了弥补这一护理缺口，并以大规模的方式解决这些问题，我们构建了NeuroPal，这是一个大型语言模型（LLM）助手，提供可扩展且基于证据的干预措施，具有三个经过临床验证的模块：（1）睡眠昼夜节律规划器，用于输出个性化昼夜节律校正协议；（2）基于认知行为疗法（CBT）和人本主义原则的认知行为重塑引擎，以改变负面归因偏差；（3）生化调节顾问，用于输出植物疗法配方以调节睡眠-代谢-肠道轴失衡。 与北京协和医院和中南大学湘雅医院合作，我们针对513名患有情绪/焦虑障碍的参与者进行了随机对照试验（RCT），显示主要终点指标有统计学上的显著改善（> p<.01）。 实验结果显示，匹兹堡睡眠质量指数（PSQI）下降了37.2%，积极情感词汇使用率（LIWC分析）上升了28.6%，患者报告的消化舒适度提高了23.4%。 此外，该助手的依从率达到89.1%，明显高于匹配对照组中由人工指导的治疗干预（72.3%）。 我们的研究结果表明，LLM驱动的多模态干预能够成功弥合时间受限的临床实践，同时保持治疗效果。 下一步，我们计划探索纵向结果跟踪以及FDA/CFDA认证途径。 显示英文摘要

摘要： Due to time constraints, mental health professionals in China are unable to offer patients prolonged talk therapy, leaving a gap in care for patients with psychological disorders, including aberrant sleep and eating patterns, maladaptive explanatory styles, and gastrointestinal dysfunction. To bridge this gap in care and address these problems in a large-scale manner, we built NeuroPal, a large language model (LLM)-assistant that provides scalable, evidence-based interventions with three clinically validated modules: (1) a sleep chronotherapy planner to output personalized circadian rhythm correction protocols, (2) a cognitive-behavioral reframing engine grounded in CBT and humanistic principles to shift negative attributional biases, and (3) a biochemical regulation advisor to output phytotherapy formulations to regulate sleep-metabolism-gut-axis imbalances. In collaboration with Peking Union Medical College Hospital and Xiangya Hospital Central South University, we ran an RCT protocol with 513 participants with mood/anxiety disorders and showed statistically significant improvements towards primary endpoints (> p<.01). Experiment shows 37.2% drop in the Pittsburgh Sleep Quality Index (PSQI), 28.6% rise in positive affective word usages (LIWC analysis), and 23.4% improvement in patient-reported digestive comfort. The assistant also reached 89.1% adherence rates, significantly higher than the human-guided therapy intervention (72.3%) in matched controls. Our results indicate that an LLM-driven multimodal intervention is able to successfully bridge time-constrained clinical practice while preserving therapeutic effectiveness. For next steps, we plan to explore longitudinal outcome tracking and FDA/CFDA certification routes.