症状网络画像模式驱动下的艾滋病感染者核心症状挖掘与精准干预研究

HIV病毒感染、抗逆转录药物、机会性感染和合并症都会引起艾滋病感染者一系列躯体和心理症状。既往相关研究主要从单一症状的视角进行调查和策略构建，忽略了真实世界下艾滋病感染者同时存在多个症状之间的相互作用，最终导致症状管理效率低且同质化严重。因此，如何利用大量随访数据，挖掘症状网络机制上的核心症状，为感染者提供个体化和精准化的症状管理支持？这是目前亟待解决的问题。本研究基于艾滋病整合式症状管理理论，开发艾滋病感染者症状网络画像模式和精准管理策略。分为四个阶段：第一，开展纵向研究，构建基于贝叶斯症状追踪症状网络预测模型；第二，发展基于证据的针对核心症状的精准干预策略和配套资源；第三，在艾滋病感染者症状自我管理APP中实现症状网络画像模块；最后，开展干预性研究验证该模式的有效性分析和两阶段的可用性检验。本课题以期形成智能化、个体化和精准化的症状管理新策略，提升症状管理效率，节约医疗资源。

HIV infection, antiretroviral therapy, opportunistic infections, and chronic comorbidities can all cause a series of physical and psychological symptoms in people living with HIV (PLWH). Previous studies found the majority of studies mainly focused on a single symptom and neglected the interactions between multiple symptoms in PLWH. Therefore, how to use medical big data on PLWH to figure out the core symptom based on symptom network and provide individualized and précised symptom management strategies for PLWH is a problem that needs to be solved in the near future. The current project based on the HIV/AIDS Integrated Symptom Management Theory aims to develop the HIV/AIDS Symptom NETwork User Persona System (HAS-NET-UPS) and precise management strategies targeting core symptoms of PLWH. The project will be divided into four stages. First, we will conduct a 12-month longitudinal study and will develop a Bayesian symptom tracking symptom network prediction model to capture the changes of symptom networks over time. Second, evidence-based strategies will be developed and will be embedded into HAS-NET-UPS. Third, we will implement HAS-NET-UPS in our previously developed APP for PLWH. Finally, a pre-post experimental study will be conducted to examine the effectiveness and usability of HAS-NET-UPS and precise management strategy in PLWH.