Abstract
This article is oriented to the research of the vehicle-mounted intelligent traditional Chinese medicine (TCM) syndrome differentiation system to provide a quick and accurate diagnosis suggestion for smart and mobile medical vehicles. Nowadays, TCM modernization has attached more and more attention due to its remarkable clinical effect. The typical TCM diagnosis process is to induces the syndrome types of a patient from the four-diagnosis symptoms based on syndrome differentiation theory, that is, given a set of symptoms to treat, an overall syndrome representation is learned by fusing all the symptoms effectively to mimic how a doctor induce the syndromes. Therefore, we believe that an overall description of symptoms of a patient is very important for the follow-up treatment and should be handled carefully and properly. However, due to the complexity and diversity of syndromes, most recommended prescription of a patient lacks the explicit ground-truth of syndrome. Therefore, in this article, a new TCM syndrome differentiation method based on multi-label classification method and deep learning method was proposed to learn the implicit symptoms induction process in the real diagnosis. A Deep Belief Network (DBN) was used to reconstruct the TCM diagnosis model based on the Rrestricted Boltzman Machine (RBM) mechanism. Towards symptoms-syndrome groups learning, a symptom vector representing the symptom collection of a patient is constructed as the input of DBN, which was used to train the symptom data and labeled samples to build an unsupervised model at first. And then the parameters of DBN was adjusted gradually based on back propagation method. Secondly, binary classification algorithm was used to convert the multi-label classification problem into several corresponding labels. Each binary classifier model corresponds to a binary classifier to complete the classification from symptoms to corresponding syndrome types. Finally, Experiments were conducted on a self-built TCM clinical records database, demonstrating obvious improvements on the classification accuracy and convergence rate. Further studies should focus on the promotion of effectiveness of the proposed model to be more in line with the actual needs of onboard medical diagnosis and treatment.
Highlights
I N the new century, with the rapid development of internet technology and the introduction of national policies and regulations related to Internet+medical and health care, mobile diagnosis and treatment has been more and more widely used in Chinese residents’ daily health services, and intelligent medical vehicles have emerged at the right time.Intelligent mobile vehicles can assist doctors to collect blood pressure, body temperature and other vital signs information, to assist medical institutions to expand their service radius to the grass-roots organization such as community, village and nursing home and so on, to strengthen the bottom construction of the medical big data of China
The proposed algorithm firstly utilizes the binary association classification to convert the multiple label classification problem into the multiple corresponding binary classifiers, converts the multi-label into a single label, and combines the deep belief network for each The classification model obtained from syndrome training solves the complex relationship between symptoms and syndrome types
This article introduces a traditional Chinese medicine (TCM) clinical syndrome differentiation algorithm which is applied to the vehicle-mounted intelligent medical system to provide a fast and accurate diagnosis suggestion
Summary
I N the new century, with the rapid development of internet technology and the introduction of national policies and regulations related to Internet+medical and health care, mobile diagnosis and treatment has been more and more widely used in Chinese residents’ daily health services, and intelligent medical vehicles have emerged at the right time. What’s more, online learning method makes the useful knowledge increase as the algorithms run [12], that is, on the basis of learning traditional Chinese physicians’ experience, the constructed deep TCM syndrome differentiation model can continue to explore conduct various prescriptions that have tried in the past and predict the effectiveness of herbs in advance. For this reason, it can be foreseeable that the TCM diagnosis and treatment machines will be smarter than human individuals and teams in the near future [10].
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