利用SHT主题模型发现证药关系

Q3 Biochemistry, Genetics and Molecular Biology

IPSJ Transactions on Bioinformatics Pub Date : 2017-01-01 DOI:10.2197/IPSJTBIO.10.16

Lidong Wang, Keyong Hu, Xiaodong Xu

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引用次数: 1

摘要

在过去的几十年里，人们通过计算机科学的各种方法对中医进行了广泛的研究，但没有人挖掘大量的临床病例来发现症状与草药之间有意义的治疗模式。为了应对这一挑战，我们探索了临床病例中非结构化和复杂的经验数据，并提出了一种通过引入新的主题模型SHT(症候-草药主题模型)来发现治疗模式的方法。组合规则被纳入到学习过程中。对3765例中医临床病例进行了评价。通过与LDA模型和LinkLDA模型的比较，验证了该方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Discovering Symptom-herb Relationship by Exploiting SHT Topic Model

TCM has been widely researched through various methods in computer science in past decades, but none digs into huge amount of clinical cases to discover the meaningful treatment patterns between symptoms and herbs. To meet the challenge, we explore the unstructured and intricate experiential data in clinical case, and propose a method to discover the treatment patterns by introducing a novel topic model named SHT (Symptom-Herb Topic model). Combinational rules are incorporated into the learning process. We evaluate our method on 3,765 TCM clinical cases. The experiment validates the effectiveness of our method compared with LDA model and LinkLDA model.

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来源期刊

IPSJ Transactions on Bioinformatics Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (miscellaneous)

CiteScore

1.90

自引率

0.00%

发文量