Foundations of statistical natural language processing
Foundations of statistical natural language processing
On the use of words and n-grams for Chinese information retrieval
IRAL '00 Proceedings of the fifth international workshop on on Information retrieval with Asian languages
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Chinese Word Segmentation and Named Entity Recognition: A Pragmatic Approach
Computational Linguistics
Bidirectional inference with the easiest-first strategy for tagging sequence data
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Artificial Intelligence in Medicine
Computational methods for Traditional Chinese Medicine: A survey
Computer Methods and Programs in Biomedicine
Latent tree models and diagnosis in traditional Chinese medicine
Artificial Intelligence in Medicine
A self-learning expert system for diagnosis in traditional Chinese medicine
Expert Systems with Applications: An International Journal
Methodological Review: Text mining for traditional Chinese medical knowledge discovery: A survey
Journal of Biomedical Informatics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Developing a robust part-of-speech tagger for biomedical text
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
Journal of Biomedical Informatics
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Automatic diagnosis is one of the most important parts in the expert system of traditional Chinese medicine (TCM), and in recent years, it has been studied widely. Most of the previous researches are based on well-structured datasets which are manually collected, structured and normalized by TCM experts. However, the obtained results of the former work could not be directly and effectively applied to clinical practice, because the raw free-text clinical records differ a lot from the well-structured datasets. They are unstructured and are denoted by TCM doctors without the support of authoritative editorial board in their routine diagnostic work. Therefore, in this paper, a novel framework of automatic diagnosis of TCM utilizing raw free-text clinical records for clinical practice is proposed and investigated for the first time. A series of appropriate methods are attempted to tackle several challenges in the framework, and the Naive Bayes classifier and the Support Vector Machine classifier are employed for TCM automatic diagnosis. The framework is analyzed carefully. Its feasibility is validated through evaluating the performance of each module of the framework and its effectiveness is demonstrated based on the precision, recall and F-Measure of automatic diagnosis results.