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      End-to-End Models to Imitate Traditional Chinese Medicine Syndrome Differentiation in Lung Cancer Diagnosis: Model Development and Validation


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          Traditional Chinese medicine (TCM) has been shown to be an efficient mode to manage advanced lung cancer, and accurate syndrome differentiation is crucial to treatment. Documented evidence of TCM treatment cases and the progress of artificial intelligence technology are enabling the development of intelligent TCM syndrome differentiation models. This is expected to expand the benefits of TCM to lung cancer patients.


          The objective of this work was to establish end-to-end TCM diagnostic models to imitate lung cancer syndrome differentiation. The proposed models used unstructured medical records as inputs to capitalize on data collected for practical TCM treatment cases by lung cancer experts. The resulting models were expected to be more efficient than approaches that leverage structured TCM datasets.


          We approached lung cancer TCM syndrome differentiation as a multilabel text classification problem. First, entity representation was conducted with Bidirectional Encoder Representations from Transformers and conditional random fields models. Then, five deep learning–based text classification models were applied to the construction of a medical record multilabel classifier, during which two data augmentation strategies were adopted to address overfitting issues. Finally, a fusion model approach was used to elevate the performance of the models.


          The F1 score of the recurrent convolutional neural network (RCNN) model with augmentation was 0.8650, a 2.41% improvement over the unaugmented model. The Hamming loss for RCNN with augmentation was 0.0987, which is 1.8% lower than that of the same model without augmentation. Among the models, the text-hierarchical attention network (Text-HAN) model achieved the highest F1 scores of 0.8676 and 0.8751. The mean average precision for the word encoding–based RCNN was 10% higher than that of the character encoding–based representation. A fusion model of the text-convolutional neural network, text-recurrent neural network, and Text-HAN models achieved an F1 score of 0.8884, which showed the best performance among the models.


          Medical records could be used more productively by constructing end-to-end models to facilitate TCM diagnosis. With the aid of entity-level representation, data augmentation, and model fusion, deep learning–based multilabel classification approaches can better imitate TCM syndrome differentiation in complex cases such as advanced lung cancer.

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          Bag of Tricks for Efficient Text Classification

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            Syndrome differentiation in modern research of traditional Chinese medicine.

            Syndrome differentiation (Bian Zheng) in traditional Chinese medicine (TCM) is the comprehensive analysis of clinical information gained by the four main diagnostic TCM procedures: observation, listening, questioning, and pulse analysis, and it is used to guide the choice of treatment either by acupuncture and/or TCM herbal formulae, that is, Fufang. TCM syndrome differentiation can be used for further stratification of the patients' conditions with certain disease, identified by orthodox medical diagnosis, which could help the improvement of efficacy of the selected intervention. In modern TCM research it is possible to integrate syndrome differentiation with orthodox medical diagnosis leading to new scientific findings in overall medical diagnosis and treatment. In this review, the focus is to screen published evidence on the role of syndrome differentiation in modern TCM research with particular emphasis on basic and clinical research as well as, pharmacological evaluation of TCM herbal formulary for drug discovery. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
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              Traditional Chinese medicine and cancer: History, present situation, and development

              Cancer treatment with traditional Chinese medicine (TCM) has a long history. Heritage provides general conditions for the innovation and development of TCM in oncology. This article reviews the development of TCM in oncology, interprets the position and function of TCM for cancer prevention and treatment, summarizes the innovations of TCM in oncology over nearly fifty years, and suggests the development direction.

                Author and article information

                JMIR Med Inform
                JMIR Med Inform
                JMIR Medical Informatics
                JMIR Publications (Toronto, Canada )
                June 2020
                16 June 2020
                : 8
                : 6
                : e17821
                [1 ] Second School of Clinic Medicine Guangzhou University of Chinese Medicine Guangzhou China
                [2 ] School of Artifical Intelligence and Information Techology Nanjing University of Chinese Medicine Nanjing China
                [3 ] Shanghai Bright AI Co, Ltd Shanghai China
                [4 ] Shanghai Literature Institute of Traditional Chinese Medicine Shanghai China
                Author notes
                Corresponding Author: Tao Yang taoyang1111@ 123456126.com
                Author information
                ©Ziqing Liu, Haiyang He, Shixing Yan, Yong Wang, Tao Yang, Guo-Zheng Li. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 16.06.2020.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

                : 15 January 2020
                : 25 February 2020
                : 30 March 2020
                : 11 April 2020
                Original Paper
                Original Paper

                traditional chinese medicine,syndrome differentiation,lung cancer,medical record,deep learning,model fusion


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