Symptom Distribution Regularity of Insomnia: Network and Spectral Clustering Analysis
Symptom Distribution Regularity of Insomnia: Network and Spectral Clustering Analysis
复制标题
失眠症状分布规律：网络与谱聚类分析
DOI：
10.2196/16749
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发表时间：
2020-04-01
影响因子：
3.2
通讯作者：
Huang, Panpan
中科院分区：
文献类型：
--
作者：
Hu, Fang;Li, Liuhuan;Huang, Panpan
背景：最近对机器学习技术的研究在各个研究领域取得了重大进展。特别是利用该方法进行知识发现已成为中医药研究的热点。症状作为患者的关键临床表现，在临床诊断和治疗中起着重要作用，显然有其潜在的中医机制。目的：探讨失眠诊断的核心症状及其潜在规律，揭示失眠的关键症状、症状之间的潜在关系及其对应证候。方法：从真实电子病历中提取807个样本的失眠数据集。参照证候对主题数据进行清理和选择后，运用复杂网络理论构建症状网络分析模型。我们采用节点中心性的四个评价指标，从多个方面发现核心症状节点。为了探索症状之间的隐藏关系，我们使用Skip-Gram模型和节点嵌入理论对网络中的每个症状节点进行训练，获得症状嵌入表示。在以数字矢量格式获取症状词汇表后，我们计算任意两个症状嵌入之间的相似性，并使用谱聚类算法将这些症状嵌入聚类为五个社区。结果：采用节点中心性评价指标，识别出入睡困难、夜间易醒、烦躁易怒、健忘、精神无力等五大失眠诊断核心症状。构建了具有隐含关系的症状嵌入，可作为今后失眠研究的基础数据集。将症状网络划分为5个社区，并将这些症状准确归类到相应的证候中。结论：这些结果表明，网络和聚类分析可以客观有效地发现关键症状和症状之间的关系。进一步识别失眠的症状分布和症状聚类，为临床诊断和治疗提供有价值的指导。
Background: Recent research in machine-learning techniques has led to significant progress in various research fields. In particular, know ledge discovery using this method has become a hot topic in traditional Chinese medicine. As the key clinical manifestations of patients, symptoms play a significant role in clinical diagnosis and treatment, which evidently have their underlying traditional Chinese medicine mechanisms.Objective: We aimed to explore the core symptoms and potential regularity of symptoms for diagnosing insomnia to reveal the key symptoms, hidden relationships underlying the symptoms, and their corresponding syndromes.Methods: An insomnia dataset with 807 samples was extracted from real-world electronic medical records. After cleaning and selecting the theme data referring to the syndromes and symptoms, the symptom network analysis model was constructed using complex network theory. We used four evaluation metrics of node centrality to discover the core symptom nodes from multiple aspects. To explore the hidden relationships among symptoms, we trained each symptom node in the network to obtain the symptom embedding representation using the Skip-Gram model and node embedding theory. After acquiring the symptom vocabulary in a digital vector format, we calculated the similarities between any two symptom embeddings, and clustered these symptom embeddings into five communities using the spectral clustering algorithm.Results: The top five core symptoms of insomnia diagnosis, including difficulty falling asleep, easy to wake up at night, dysphoria and irascibility, forgetful, and spiritlessness and weakness, were identified using evaluation metrics of node centrality. The symptom embeddings with hidden relationships were constructed, which can be considered as the basic dataset for future insomnia research. The symptom network was divided into five communities, and these symptoms were accurately categorized into their corresponding syndromes.Conclusions: These results highlight that network and clustering analyses can objectively and effectively find the key symptoms and relationships among symptoms. Identification of the symptom distribution and symptom clusters of insomnia further provide valuable guidance for clinical diagnosis and treatment.