呼吸机依赖是慢性疾病（简称慢病）患者预后不良的主要原因，已成为重要的公共健康问题。慢病患者呼吸机依赖的风险因素复杂且不断变化，因缺乏智能预测模型，难以提前识别高危人群并进行精准干预，慢病患者呼吸机依赖发生率及病死率均较高。通过自动提取与融合呼吸、脑、心、膈肌及内分泌等多维度器官功能特征，构建自适应优化预测模型，形成兼具普适性与特异性的智能化表型预测体系。在此基础上，开发模块化、临床表型导向的干预策略，并依托高频数据反馈与虚拟现实技术，构建覆盖有创及无创呼吸支持的智慧化康复平台。通过进一步制定从呼吸支持到远期康复的全周期诊疗路径，形成“监测-反馈-干预”闭环，推动我国呼吸康复诊疗体系从传统经验模式向数据驱动、个体化、智能化的新范式转型。

Prolonged mechanical ventilation is a major cause of poor prognosis in patients with chronic diseases and has become a significant public health issue. The risk factors for prolonged mechanical ventilation in chronic disease patients are complex and constantly changing. Due to the lack of intelligent prediction models, it is difficult to identify high-risk populations in advance and provide precise interventions, resulting in high incidence and mortality rates of prolonged mechanical ventilation among these patients. By automatically extracting and integrating multidimensional organ functional features-including respiratory, neurological, cardiac, diaphragmatic, and endocrine metrics-an adaptive, optimized predictive model is constructed to form an intelligent phenotype prediction system that balances universality with specificity. Based on this, modular and clinical phenotype-oriented intervention strategies are developed. Furthermore, by leveraging high-frequency data feedback and virtual reality technology, an intelligent respiratory rehabilitation platform is built to cover both invasive and non-invasive support. The establishment of a full-cycle clinical pathway, extending from initial respiratory support to long-term rehabilitation, creates a "monitoring-feedback-intervention" closed loop, driving the transformation of the respiratory rehabilitation system from a traditional experience-based model toward a new, data-driven, individualized, and intelligent paradigm.