Mc-eLDA: Towards pathogenesis analysis in traditional chinese medicine by multi-content embedding LDA

Ying Zhang; Wendi Ji; Haofen Wang; Xiaoling Wang; Jin Chen

doi:10.1007/978-3-030-16148-4_38

Mc-eLDA: Towards pathogenesis analysis in traditional chinese medicine by multi-content embedding LDA

Ying Zhang, Wendi Ji, Haofen Wang, Xiaoling Wang, Jin Chen

Internal Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Traditional Chinese medicine (TCM) is well-known for its unique theory and effective treatment for complicated diseases. In TCM theory, “pathogenesis” is the cause of patient’s disease symptoms and is the basis for prescribing herbs. However, the essence of pathogenesis analysis is not well depicted by current researches. In this paper, we propose a novel topic model called Multi-Content embedding LDA (MC-eLDA), aiming to collaboratively capture the relationships of symptom-pathogenesis-herb triples, relationship between symptom-symptom, and relationship between herb-herb, which can be used in auxiliary diagnosis and treatment. By projecting discrete symptom words and herb words into two continuous semantic spaces respectively, the semantic equivalence can be encoded by exploiting the contiguity of their corresponding embeddings. Compared with previous models, topic coherence in each pathogenesis cluster can be promoted. Pathogenesis structures that previous topic modeling can not capture can be discovered by MC-eLDA. Then a herb prescription recommendation method is conducted based on MC-eLDA. Experimental results on two real-world TCM medical cases datasets demonstrate the effectiveness of the proposed model for analyzing pathogenesis as well as helping make diagnosis and treatment in clinical practice.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings
Editors	Zhi-Hua Zhou, Min-Ling Zhang, Qiang Yang, Sheng-Jun Huang, Zhiguo Gong
Pages	489-500
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-030-16148-4_38
State	Published - 2019
Event	23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019 - Macau, China Duration: Apr 14 2019 → Apr 17 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11439 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019
Country/Territory	China
City	Macau
Period	4/14/19 → 4/17/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

Keywords

Embedding
Topic modeling
Traditional Chinese medicine

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-16148-4_38

Cite this

Zhang, Y., Ji, W., Wang, H., Wang, X., & Chen, J. (2019). Mc-eLDA: Towards pathogenesis analysis in traditional chinese medicine by multi-content embedding LDA. In Z.-H. Zhou, M.-L. Zhang, Q. Yang, S.-J. Huang, & Z. Gong (Eds.), Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings (pp. 489-500). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11439 LNAI). https://doi.org/10.1007/978-3-030-16148-4_38

Zhang, Ying ; Ji, Wendi ; Wang, Haofen et al. / Mc-eLDA : Towards pathogenesis analysis in traditional chinese medicine by multi-content embedding LDA. Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings. editor / Zhi-Hua Zhou ; Min-Ling Zhang ; Qiang Yang ; Sheng-Jun Huang ; Zhiguo Gong. 2019. pp. 489-500 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{aa713423b37d418493e046ac3d899166,

title = "Mc-eLDA: Towards pathogenesis analysis in traditional chinese medicine by multi-content embedding LDA",

abstract = "Traditional Chinese medicine (TCM) is well-known for its unique theory and effective treatment for complicated diseases. In TCM theory, “pathogenesis” is the cause of patient{\textquoteright}s disease symptoms and is the basis for prescribing herbs. However, the essence of pathogenesis analysis is not well depicted by current researches. In this paper, we propose a novel topic model called Multi-Content embedding LDA (MC-eLDA), aiming to collaboratively capture the relationships of symptom-pathogenesis-herb triples, relationship between symptom-symptom, and relationship between herb-herb, which can be used in auxiliary diagnosis and treatment. By projecting discrete symptom words and herb words into two continuous semantic spaces respectively, the semantic equivalence can be encoded by exploiting the contiguity of their corresponding embeddings. Compared with previous models, topic coherence in each pathogenesis cluster can be promoted. Pathogenesis structures that previous topic modeling can not capture can be discovered by MC-eLDA. Then a herb prescription recommendation method is conducted based on MC-eLDA. Experimental results on two real-world TCM medical cases datasets demonstrate the effectiveness of the proposed model for analyzing pathogenesis as well as helping make diagnosis and treatment in clinical practice.",

keywords = "Embedding, Topic modeling, Traditional Chinese medicine",

author = "Ying Zhang and Wendi Ji and Haofen Wang and Xiaoling Wang and Jin Chen",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019 ; Conference date: 14-04-2019 Through 17-04-2019",

year = "2019",

doi = "10.1007/978-3-030-16148-4_38",

language = "English",

isbn = "9783030161477",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "489--500",

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booktitle = "Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings",

}

Zhang, Y, Ji, W, Wang, H, Wang, X & Chen, J 2019, Mc-eLDA: Towards pathogenesis analysis in traditional chinese medicine by multi-content embedding LDA. in Z-H Zhou, M-L Zhang, Q Yang, S-J Huang & Z Gong (eds), Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11439 LNAI, pp. 489-500, 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, Macau, China, 4/14/19. https://doi.org/10.1007/978-3-030-16148-4_38

Mc-eLDA: Towards pathogenesis analysis in traditional chinese medicine by multi-content embedding LDA. / Zhang, Ying; Ji, Wendi; Wang, Haofen et al.
Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings. ed. / Zhi-Hua Zhou; Min-Ling Zhang; Qiang Yang; Sheng-Jun Huang; Zhiguo Gong. 2019. p. 489-500 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11439 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Mc-eLDA

T2 - 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019

AU - Zhang, Ying

AU - Ji, Wendi

AU - Wang, Haofen

AU - Wang, Xiaoling

AU - Chen, Jin

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - Traditional Chinese medicine (TCM) is well-known for its unique theory and effective treatment for complicated diseases. In TCM theory, “pathogenesis” is the cause of patient’s disease symptoms and is the basis for prescribing herbs. However, the essence of pathogenesis analysis is not well depicted by current researches. In this paper, we propose a novel topic model called Multi-Content embedding LDA (MC-eLDA), aiming to collaboratively capture the relationships of symptom-pathogenesis-herb triples, relationship between symptom-symptom, and relationship between herb-herb, which can be used in auxiliary diagnosis and treatment. By projecting discrete symptom words and herb words into two continuous semantic spaces respectively, the semantic equivalence can be encoded by exploiting the contiguity of their corresponding embeddings. Compared with previous models, topic coherence in each pathogenesis cluster can be promoted. Pathogenesis structures that previous topic modeling can not capture can be discovered by MC-eLDA. Then a herb prescription recommendation method is conducted based on MC-eLDA. Experimental results on two real-world TCM medical cases datasets demonstrate the effectiveness of the proposed model for analyzing pathogenesis as well as helping make diagnosis and treatment in clinical practice.

AB - Traditional Chinese medicine (TCM) is well-known for its unique theory and effective treatment for complicated diseases. In TCM theory, “pathogenesis” is the cause of patient’s disease symptoms and is the basis for prescribing herbs. However, the essence of pathogenesis analysis is not well depicted by current researches. In this paper, we propose a novel topic model called Multi-Content embedding LDA (MC-eLDA), aiming to collaboratively capture the relationships of symptom-pathogenesis-herb triples, relationship between symptom-symptom, and relationship between herb-herb, which can be used in auxiliary diagnosis and treatment. By projecting discrete symptom words and herb words into two continuous semantic spaces respectively, the semantic equivalence can be encoded by exploiting the contiguity of their corresponding embeddings. Compared with previous models, topic coherence in each pathogenesis cluster can be promoted. Pathogenesis structures that previous topic modeling can not capture can be discovered by MC-eLDA. Then a herb prescription recommendation method is conducted based on MC-eLDA. Experimental results on two real-world TCM medical cases datasets demonstrate the effectiveness of the proposed model for analyzing pathogenesis as well as helping make diagnosis and treatment in clinical practice.

KW - Embedding

KW - Topic modeling

KW - Traditional Chinese medicine

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U2 - 10.1007/978-3-030-16148-4_38

DO - 10.1007/978-3-030-16148-4_38

M3 - Conference contribution

AN - SCOPUS:85064894265

SN - 9783030161477

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings

A2 - Zhou, Zhi-Hua

A2 - Zhang, Min-Ling

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A2 - Huang, Sheng-Jun

A2 - Gong, Zhiguo

Y2 - 14 April 2019 through 17 April 2019

ER -

Zhang Y, Ji W, Wang H, Wang X, Chen J. Mc-eLDA: Towards pathogenesis analysis in traditional chinese medicine by multi-content embedding LDA. In Zhou ZH, Zhang ML, Yang Q, Huang SJ, Gong Z, editors, Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings. 2019. p. 489-500. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-16148-4_38

Mc-eLDA: Towards pathogenesis analysis in traditional chinese medicine by multi-content embedding LDA

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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