Abstract：
Eating disorders are serious psychiatric illnesses with treatments ineffective for about 50% of individuals due to high heterogeneity of symptom presentation even within the same diagnoses, a lack of personalized treatments to address this heterogeneity, and the fact that clinicians are left to rely upon their own judgment to decide how to personalize treatment. Idiographic (personalized) networks can be estimated from ecological momentary assessment data, and have been used to investigate central symptoms, which are theorized to be fruitful treatment targets. However, both efficacy of treatment target selection and implementation with \'real world\' clinicians could be maximized if clinician input is integrated into such networks. An emerging line of research is therefore proposing to integrate case conceptualizations and statistical routines, tying together the benefits from clinical expertise as well as patient experience and idiographic networks. The current pilot compares personalized treatment implications from different approaches to constructing idiographic networks. For two patients with a diagnosis of anorexia nervosa, we compared idiographic networks 1) based on the case conceptualization from clinician and patient, 2) estimated from patient EMA data (the current default in the literature), and 3) based on a combination of case conceptualization and patient EMA data networks, drawing on informative priors in Bayesian inference. Centrality-based treatment recommendations differed to varying extent between these approaches for patients. We discuss implications from these findings, as well as how these models may inform clinical practice by pairing evidence-based treatments with identified treatment targets.
摘要：
进食障碍是严重的精神疾病，由于症状表现的高度异质性，即使在相同的诊断中，约50％的个体的治疗无效。缺乏解决这种异质性的个性化治疗，以及临床医生依靠自己的判断来决定如何个性化治疗的事实。可以从生态瞬时评估数据中估计个性化(个性化)网络，并被用来调查中枢症状，这些理论被认为是富有成效的治疗目标。然而,如果将临床医生的输入整合到这样的网络中,那么治疗目标选择和临床医生实施的效果都可以最大化.因此，一个新兴的研究方向是提出整合案例概念化和统计例程，将临床专业知识以及患者经验和具体网络的好处结合在一起。当前的试点比较了构建具体网络的不同方法对个性化治疗的影响。对于两名诊断为神经性厌食症的患者，我们比较了具体网络1)基于临床医生和患者的病例概念化，2)根据患者EMA数据(文献中的当前默认值)估计，和3)基于病例概念化和患者EMA数据网络的组合，在贝叶斯推理中借鉴信息先验。对于患者，基于中心的治疗建议在这些方法之间有不同程度的差异。我们讨论了这些发现的含义，以及这些模型如何通过将循证治疗与已确定的治疗目标配对，为临床实践提供信息。
