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Prescription Function Prediction Using Topic Model and Multilabel Classifiers
Lidong Wang1, Yin Zhang2, Yun Zhang3
1Qianjiang College, Hangzhou Normal University, Hangzhou 310018, China.
Evidence-Based Complementary and Alternative Medicine : Ecam
|December 14, 2017
Summary
Predicting the function of new Traditional Chinese Medicine (TCM) prescriptions is now possible. Two computational methods, a supervised topic model and multilabel classifiers, were developed and tested, with classifiers showing slightly better performance for novel prescription discovery.
Area of Science:
- Computational methods in Traditional Chinese Medicine (TCM).
- Herbal medicine informatics.
- Pharmacological prediction modeling.
Background:
- Determining the therapeutic function of Traditional Chinese Medicine (TCM) prescriptions is a complex challenge.
- Existing computational research in TCM has not focused on predicting the function of novel prescriptions.
Purpose of the Study:
- To develop and evaluate computational methods for predicting the function of new TCM prescriptions.
- To provide tools for discovering new prescriptions before clinical trials.
Main Methods:
- A novel supervised topic model, Label-Prescription-Herb (LPH), incorporating herb-herb compatibility rules.
- Multilabel classifiers utilizing TFIDF features and herbal attribute features.
Main Results:
- Both developed methods demonstrated good performance in predicting prescription function.
- Multilabel classifiers slightly outperformed the LPH-based supervised topic model.
Conclusions:
- The developed computational approaches offer valuable insights for identifying potential functions of new TCM prescriptions.
- These methods can aid in the discovery of novel prescriptions, potentially reducing pre-clinical testing efforts.
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