Symptom Distribution Regularity of Insomnia: Network and Spectral Clustering Analysis.

Fang Hu,Xiaoyu Huang,Panpan Huang,Xingyu Yan,Liuhuan Li

doi:10.2196/16749

Abstract

BackgroundRecent research in machine-learning techniques has led to signiﬁcant progress in various research ﬁelds. In particular, knowledge 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.ObjectiveWe 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.MethodsAn 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.ResultsThe 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.ConclusionsThese 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.

Highlights

BackgroundInsomnia is a subjective complaint of a sleep disorder in which the patient has difficulty falling asleep or remaining asleep as long as desired
The most fundamental reason for this lack is that the clinical manifestations of insomnia are complicated and diverse; traditional Chinese medicine (TCM) physicians have difficulties in accurately extracting the core symptoms to carry out effective treatment according to clinical characteristic categories
Based on the symptom network of insomnia, the core symptoms can be identified accurately using these evaluation metrics referring to multiple aspects

Summary

Introduction

BackgroundInsomnia is a subjective complaint of a sleep disorder in which the patient has difficulty falling asleep or remaining asleep as long as desired. The most fundamental reason for this lack is that the clinical manifestations of insomnia are complicated and diverse; TCM physicians have difficulties in accurately extracting the core symptoms to carry out effective treatment according to clinical characteristic categories. As the key clinical manifestations of patients, symptoms play a significant role in clinical diagnosis and treatment, which have their underlying traditional Chinese medicine mechanisms. 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. 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. Identification of the symptom distribution and symptom clusters of insomnia further provide valuable guidance for clinical diagnosis and treatment

Objectives

Methods

Results

Discussion

Conclusion