Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2025
Study on subtyping and Traditional Chinese Medicine treatment of depression based on machine learning and text mining.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Mengyue Fan, Lin Yao, Guoqing Zhang, Ruixue Wang, Kexin Chen, Yujing Fan, Ziming Wang, Jia F U, Yongjun Chen, Taiyi Wang
Аннотация
OBJECTIVE: To research the subtyping and treatment of depression by leveraging studying on extensive Traditional Chinese Medicine (TCM) experiences through artificial intelligence (AI).
METHODS: We retrieved depression-related literature published from inception to April 2023 from databases. From these sources, we extracted symptoms, signs, and prescriptions associated with depression. By utilizing the tree number system in the medical subject headings (MeSH), we established a hierarchical relationship matrix for symptoms/signs, as well as depression sample fingerprints. Using an unsupervised clustering algorithm, we constructed a machine learning model for classifying depression patients. Furthermore, we conducted an analysis of medication rules for each depression cluster.
RESULTS: We created a My Structured Query Language (MySQL) database containing datasets of depression-symptoms/signs and depression-herbs, through mining 3522 published clinical literatures on TCM diagnosis and treatment for depression. We established hierarchical relationships among symptoms/signs of depression patients. Our unsupervised clustering analysis revealed that depression patients could be classified into 9 subtypes, with each subtype corresponding to a specific treatment prescription. Notably, one of the depression subtypes was consistently treated by Qi-tonifying formulas and herbs. This finding was further supported by data from Qi-deficiency patients, as there was a high similarity in the top symptoms/signs shared between this subtype and Qi-deficiency diagnosed by TCM.
CONCLUSIONS: This study identified the subtypes and TCM treatment of depression by using machine learning and text mining.
METHODS: We retrieved depression-related literature published from inception to April 2023 from databases. From these sources, we extracted symptoms, signs, and prescriptions associated with depression. By utilizing the tree number system in the medical subject headings (MeSH), we established a hierarchical relationship matrix for symptoms/signs, as well as depression sample fingerprints. Using an unsupervised clustering algorithm, we constructed a machine learning model for classifying depression patients. Furthermore, we conducted an analysis of medication rules for each depression cluster.
RESULTS: We created a My Structured Query Language (MySQL) database containing datasets of depression-symptoms/signs and depression-herbs, through mining 3522 published clinical literatures on TCM diagnosis and treatment for depression. We established hierarchical relationships among symptoms/signs of depression patients. Our unsupervised clustering analysis revealed that depression patients could be classified into 9 subtypes, with each subtype corresponding to a specific treatment prescription. Notably, one of the depression subtypes was consistently treated by Qi-tonifying formulas and herbs. This finding was further supported by data from Qi-deficiency patients, as there was a high similarity in the top symptoms/signs shared between this subtype and Qi-deficiency diagnosed by TCM.
CONCLUSIONS: This study identified the subtypes and TCM treatment of depression by using machine learning and text mining.