Name
Title | ||
|---|---|---|
Study On Clinical Terminology Extraction Of Traditional Chinese Medicine Based On Internal Aggregation And Boundary Degree Of Freedom Of Character Strings |
Abstract | ||
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Objective: To propose a method for clinical terminology extraction of traditional Chinese medicine (TCM). Methods: Conditional probability was used to measure the internal aggregation of terminologies while information entropy measuring the boundary degree of freedom (DOF), so as to establish a method to extract the clinical terminologies of TCM. Different model thresholds were set up to analyze the medical cases with 29588 clinic visits. Results: When he maximum length of character was 5, a total of 7237 terminologies were extracted, in which 5963 were correct, with precision rate, recall rate, and F value of 82.40%, 77.46% and 79.85%, respectively, and the model efficacy was the optimum. Conclusion: Internal aggregation combined with boundary DOF of characters can effectively extract clinical terminologies of TCM. In subsequent study, multiple methods should be combined to extract the terminologies in different parts of medical cases, so as to increase the precision of terminology extraction. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) | internal aggregation, boundary degree of freedom, TCM clinic, terminology extraction, model evaluation |
Field | DocType | ISSN |
Degrees of freedom (statistics),Recall rate,Conditional probability,Computer science,Traditional Chinese medicine,Artificial intelligence,Natural language processing,Hidden Markov model,Entropy (information theory),Machine learning,Terminology extraction | Conference | 2156-1125 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||