Research on treatment and medication rule of insomnia treated by TCM based on data mining

Ye, Yi; Xu, Bo; Ma, Li; Zhu, Jiayan; Shi, Heyuan; Cai, Xiujun

doi:10.1109/bibm47256.2019.8982941

Cited by 5 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, many disciplines witness fast growth in exploiting machine learning and text mining technologies to discover knowledge hidden in a massive volume of data. Similarly, some efforts have raised in TCM which utilize machine learning and text mining technologies for discovering knowledge from prescriptions and clinical records, such as treatment rules mining [10,11], medical term extraction [12][13][14], syndrome differentiation [15], knowledge graph construction [16] and fine-grained entity corpus construction [17]. However, majority efforts in these studies are devoted to structured data or unstructured textual data written in modern Chinese language, in spite of the importance of ancient literature for modern TCM research and clinical practice, as mentioned in Section Background.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Extracting Disease-Specific Clinical Experiences from Ancient Literature of Traditional Chinese Medicine with Deep Learning

Lu¹,

Chen²,

Zhang³

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Ancient literature of Traditional Chinese Medicine (TCM) contains massive clinical experiences which are important ingredient of TCM knowledge and valuable for TCM clinical practice of nowadays. However, it is difficult for TCM professionals to acquire such valuable experiences due to their massive volume and broad occurrence in the literature. Furthermore, different characteristics of ancient Chinese language from the modern one lead to additional challenges for analyzing the literature, regardless of how to perform the analyzing, manually or automatically with a software toolkit. Methods: In order to overcome the aforementioned challenges, we formalize a novel information extraction task for ancient literature of TCM, and the entities to be extracted are Disease-Specific Clinical Experiences (DSCEs) occurring in the literature. For the purpose, we have collected two corpora from ancient literature of TCM and annotated them manually with DSCEs occurrence information for the diseases pregnant abdominalgia and colporrhagia (妊娠腹痛及下血) and jaundice (黄疸) respectively. We further propose a deep learning and CRF-based algorithmic framework with character encoding of ancient Chinese, thus avoiding the special difficulty in word segmentation for ancient Chinese texts. We investigate the framework with different methods for contextual encoding of characters in a sentence, including CNN, Bi-LSTM and BERT, and diverse approaches to aggregate contextual information of characters into a sentence encoding, such as max-pooling and attention mechanism. After that all the encoded sentences in a section of the literature are passed through a Bi-LSTM-based sequence labelling model with CRF inference on its top to obtain an optimal label sequence for the sentences in the section. Results: We conduct a series of experiments on the two corpora to verify the effectiveness of our framework for the task, and evaluate its effectiveness with different metrics in two granularities of labelling, namely accuracy/F1-value in sentence-level labelling and precision/recall/F1-value in correct recognition of the whole DSCEs. Conclusion: The experimental results demonstrate that the deep learning and CRF-based framework with character encoding of ancient Chinese could achieve an accuracy of 80.40%±1.64% and an F1-value of 76.73%±1.59% for the sentence labelling, while for recognition of the whole DSCEs, it is able to obtain the recall of 44.97%±2.16% and the precision of 51.13%±2.64%, meaning that the framework is a promising baseline for further development of the novel information extraction task for TCM.

show abstract

Section: Related Workmentioning

confidence: 99%

“…All the model parameters in the layers of embedding, sentence encoding, sentence contextual encoding and CRF are trained jointly using the negative log-likelihood loss with the L 2 norm regularization, as shown in the formula (11).…”

Section: Crf Layermentioning

confidence: 99%