https://github.com/yao8839836/PTM

A Topic Modeling Approach for Traditional Chinese Medicine Prescriptions. TKDE 2018
https://github.com/yao8839836/PTM

herbs knowledge prescriptions symptoms topic-modeling traditional-chinese-medicine

Last synced: about 1 month ago
JSON representation

A Topic Modeling Approach for Traditional Chinese Medicine Prescriptions. TKDE 2018

• Host: GitHub
• URL: https://github.com/yao8839836/PTM
• Owner: yao8839836
• Created: 2016-02-02T09:51:39.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2019-11-30T16:54:56.000Z (about 5 years ago)
• Last Synced: 2024-11-10T14:17:23.963Z (about 1 month ago)
• Topics: herbs, knowledge, prescriptions, symptoms, topic-modeling, traditional-chinese-medicine
• Language: Java
• Homepage:
• Size: 19.8 MB
• Stars: 62
• Watchers: 2
• Forks: 26
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-topic-models - PTM - Prescription Topic Model for Traditional Chinese Medicine Prescriptions [:page_facing_up:](https://ieeexplore.ieee.org/abstract/document/8242679) (interesting benchmark models) (Models / Exotic models)

README

        
# PTM

The dataset and implementation of Prescription Topic Model in our paper:

Liang Yao, Yin Zhang, Baogang Wei, Wenjin Zhang, Zhe Jin. (2018). "A Topic Modeling Approach for Traditional Chinese Medicine Prescriptions". IEEE Transactions on Knowledge and Data Engineering (TKDE) 30(6), pp.1007-1021. 

# Require

Java 7 or above, I use Java 8 in this project.

Eclipse

# Data

The Copyright holder of the dataset is [China Knowledge

Centre for Engineering Sciences and Technology (CKCEST)](http://zcy.ckcest.cn/tcm/). The dataset is for research use only. Any commercial use, sale, or other monetization is prohibited.

98,334 raw prescriptions with herbs and symptoms are in `/data/prescriptions.txt` . Each line is for a prescription, symptoms are on the left and herbs are on the right.

The preprocessed 33,765 prescriptions: `/data/pre_herbs.txt`, `/data/pre_symptoms.txt`. 

`Training set`: `/data/pre_herbs_train.txt`, `/data/pre_symptoms_train.txt`

`Test set`: `/data/pre_herbs_test.txt`, `/data/pre_symptoms_test.txt`

Note: 

1. Each line in above files is for a prescription, the same line in `/data/pre_herbsX.txt` and `/data/pre_symptomsX.txt` (X is _train or _test or ' ' ) is for the same prescription.

2. Each number in above files means an herb or a symptom, each number is an index of the following herb list or symptom list. For example, '5' in `/file/pre_herbs_train.txt` means the 6th herb in the herb list `/data/herbs_contains.txt`, '17' in `/file/pre_symptoms_train.txt` means the 18th symptom in the symptom list `/data/symptom_contains.txt`. 

Herb list: `/data/herbs_contains.txt`

Symptom list: `/data/symptom_contains.txt`

TCM MeSH herb-symptom correspondence knowledge: `/data/symptom_herb_tcm_mesh.txt`

Symptom Category: `/data/symptom_category.txt`

# Demo

`PTM(a)`: /src/test/RunPTMa.java (reproducing prescribing patterns discovery results)

`PTM(b)`: /src/test/RunPTMb.java

`PTM(c)`: /src/test/RunPTMc.java

`PTM(d)`: /src/test/RunPTMd.java

# Herbs and symptoms prediction/recommendation tasks 

(reproducing herbs/symptoms predictive perplexity and precision@N results)

`PTM(a)`: /src/test/PTMaPredict.java

`PTM(b)`: /src/test/PTMbPredict.java

`PTM(c)`: /src/test/PTMcPredict.java

`PTM(d)`: /src/test/PTMdPredict.java

# Topic herb precision

/src/test/TopicPrecisionSymToHerb.java

# Prescription predictive perplexity

`PTM(a)`: src/perplexity/PTMaPerplexity.java

`PTM(b)`: src/perplexity/PTMbPerplexity.java

`PTM(c)`: src/perplexity/PTMcPerplexity.java

`PTM(d)`: src/perplexity/PTMdPerplexity.java

 

# Topic symptom coherence

/src/test/TopicKnowCoherence.java