Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Lugano, Switzerland.
La Roche-Posay Dermatological Laboratories, Levallois-Perret, France.
Sci Rep. 2024 May 4;14(1):10266. doi: 10.1038/s41598-024-60937-3.
The relationship between skin diseases and mental illnesses has been extensively studied using cross-sectional epidemiological data. Typically, such data can only measure association (rather than causation) and include only a subset of the diseases we may be interested in. In this paper, we complement the evidence from such analyses by learning an overarching causal network model over twelve health conditions from the Google Search Trends Symptoms public data set. We learned the causal network model using a dynamic Bayesian network, which can represent both cyclic and acyclic causal relationships, is easy to interpret and accounts for the spatio-temporal trends in the data in a probabilistically rigorous way. The causal network confirms a large number of cyclic relationships between the selected health conditions and the interplay between skin and mental diseases. For acne, we observe a cyclic relationship with anxiety and attention deficit hyperactivity disorder (ADHD) and an indirect relationship with depression through sleep disorders. For dermatitis, we observe directed links to anxiety, depression and sleep disorders and a cyclic relationship with ADHD. We also observe a link between dermatitis and ADHD and a cyclic relationship between acne and ADHD. Furthermore, the network includes several direct connections between sleep disorders and other health conditions, highlighting the impact of the former on the overall health and well-being of the patient. The average for a condition given the values of all conditions in the previous week is 0.67: in particular, 0.42 for acne, 0.85 for asthma, 0.58 for ADHD, 0.87 for burn, 0.76 for erectile dysfunction, 0.88 for scars, 0.57 for alcohol disorders, 0.57 for anxiety, 0.53 for depression, 0.74 for dermatitis, 0.60 for sleep disorders and 0.66 for obesity. Mapping disease interplay, indirect relationships, and the key role of mediators, such as sleep disorders, will allow healthcare professionals to address disease management holistically and more effectively. Even if we consider all skin and mental diseases jointly, each disease subnetwork is unique, allowing for more targeted interventions.
皮肤病和精神疾病之间的关系已经通过横断面流行病学数据进行了广泛研究。通常，此类数据只能衡量关联（而不是因果关系），并且仅包含我们可能感兴趣的疾病的一部分。在本文中，我们通过从 Google Search Trends Symptoms 公共数据集学习涵盖十二种健康状况的总体因果网络模型，补充了此类分析的证据。我们使用动态贝叶斯网络学习因果网络模型，该模型可以表示循环和非循环因果关系，易于解释，并以概率严谨的方式解释数据中的时空趋势。因果网络确认了所选健康状况之间的大量循环关系以及皮肤和精神疾病之间的相互作用。对于痤疮，我们观察到与焦虑和注意力缺陷多动障碍（ADHD）之间的循环关系，以及通过睡眠障碍与抑郁之间的间接关系。对于皮炎，我们观察到与焦虑、抑郁和睡眠障碍的有向链接，以及与 ADHD 的循环关系。我们还观察到皮炎和 ADHD 之间的联系以及痤疮和 ADHD 之间的循环关系。此外，网络中还包括睡眠障碍与其他健康状况之间的几个直接联系，突出了前者对患者整体健康和幸福感的影响。给定前一周所有条件的值，条件的平均概率为 0.67：特别是，痤疮为 0.42，哮喘为 0.85，ADHD 为 0.58，烧伤为 0.87，勃起功能障碍为 0.76，疤痕为 0.88，酒精障碍为 0.57，焦虑为 0.57，抑郁为 0.53，皮炎为 0.74，睡眠障碍为 0.60，肥胖为 0.66。映射疾病相互作用、间接关系以及睡眠障碍等中介的关键作用，将使医疗保健专业人员能够更全面、更有效地进行疾病管理。即使我们将所有皮肤和精神疾病一起考虑，每个疾病子网都是独特的，允许进行更有针对性的干预。
