Recommending prescription via tongue image to assist clinician

Wen, Gongjian; Wang, Kewen; Li, Huihui; Huang, Yuhua; Zhang, Shijun

doi:10.1007/s11042-020-10441-3

Cited by 11 publications

(8 citation statements)

References 47 publications

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“…Traditional tongue diagnosis, on the other hand, is heavily dependent on the amount of clinical expertise a clinician has; as a consequence, various doctors are likely to arrive at different diagnostic conclusions for the same patient [4]. Thankfully, computer-aided methods for tongue diagnosis are able to improve upon these shortcomings because to the utilization of computers and other approaches that are relevant to the field [5].…”

Section: Introductionmentioning

confidence: 99%

CATDSNet: Computer Aided Tongue Diagnosis System for Disease Prediction Using Hybrid Extreme Learning Machine

2023

IJIES

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Tongue diagnosis is an important way of diagnosing the human health in Indian ayurvedic medicine (IAM), which helps to identify different diseases through tongue image analysis. Traditionally, this diagnosis system has been done by ayurvedic experts to recognize the type of health issue by observing the human tongue. But the recent advancements in machine learning algorithms in medical field enabled the researchers to implement a computer aided diagnosis system for ayurvedic medical treatment. However, the existing tongue diagnosis systems are suffering with poor classification performance due to the variations in tongue appearance such as color, shape, coating, and texture properties. Therefore, this article focuses on computer aided tongue diagnosis system (CATDS) for disease predication through tongue image analysis, here after denoted as CATDSNet. Initially, fast nonlocal mean (FNLM) filtering is applied on test image to perform the preprocessing of given tongue dataset. Next, color features are extracted from the pre-processed tongue images using colour moments. In addition, grey level cooccurrence matrix (GLCM) is used to extract the texture features information. Then, this extracted color, and texture features are used to train the proposed CATDSNet using hybrid extreme learning machine (HELM) classifier for classifying various diseases such as healthy, appendicitis, bronchitis, gastritis, heart disease, and pancreatitis. The obtained simulation results on tongue image dataset disclose the superiority of proposed CATDSNet model as compared to state-of-the art approaches such as random forest, support vector machine (SVM), and SVM-based recursive feature elimination (SVM-RFE) with a classification accuracy of 92.3%, precision of 92.39%, recall of 92.06% and F1-score of 92.02%.

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Section: Introductionmentioning

confidence: 99%

CATDSNet: Computer Aided Tongue Diagnosis System for Disease Prediction Using Hybrid Extreme Learning Machine

2023

IJIES

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“…Wang [ 16 ] constructed the recommendation system based on a knowledge graph and recommended prescription by combining the diagnostic process of TCM. Wen et al [ 17 ] proposed a TCM prescription recommendation based on constructing a tongue image dataset. Jin et al [ 18 ] developed a GCN-based model for herb prediction via heterogeneous herb-symptom networks.…”

Section: Introductionmentioning

confidence: 99%

TCMPR: TCM Prescription Recommendation Based on Subnetwork Term Mapping and Deep Learning

Dong

Zheng

Shu

et al. 2022

BioMed Research International

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Traditional Chinese medicine (TCM) has played an indispensable role in clinical diagnosis and treatment. Based on a patient’s symptom phenotypes, computation-based prescription recommendation methods can recommend personalized TCM prescription using machine learning and artificial intelligence technologies. However, owing to the complexity and individuation of a patient’s clinical phenotypes, current prescription recommendation methods cannot obtain good performance. Meanwhile, it is very difficult to conduct effective representation for unrecorded symptom terms in an existing knowledge base. In this study, we proposed a subnetwork-based symptom term mapping method (SSTM) and constructed a SSTM-based TCM prescription recommendation method (termed TCMPR). Our SSTM can extract the subnetwork structure between symptoms from a knowledge network to effectively represent the embedding features of clinical symptom terms (especially the unrecorded terms). The experimental results showed that our method performs better than state-of-the-art methods. In addition, the comprehensive experiments of TCMPR with different hyperparameters (i.e., feature embedding, feature dimension, subnetwork filter threshold, and feature fusion) demonstrate that our method has high performance on TCM prescription recommendation and potentially promote clinical diagnosis and treatment of TCM precision medicine.

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“…Intelligent diagnosis, based on medical expertise and coupled with artificial intelligence to accomplish autonomous diagnosis, is one of the current hot topics for interdisciplinary study. In the context of intelligent diagnosis in TCM, researchers have completed prescription recommendations and physique identification based on the tongue or facial images (Hu et al [ 9 ], Li et al [ 10 ], Wen et al [ 11 ], Li et al [ 12 ], Liao et al [ 13 ], Hu et al [ 14 ]), feature extraction after sound acquisition (Jiang et al [ 15 ]), and designed pulse feature extraction methods for wrist pulse analysis (Lisheng et al [ 16 ], Zhang et al [ 17 ], Zhang et al [ 18 ]). Inspection, auscultation, olfaction, and palpation can be done with cameras or sensors to collect the necessary signal data, which is analyzed using artificial intelligence algorithms to achieve an intelligent diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Personalized Intelligent Syndrome Differentiation Guided By TCM Consultation Philosophy

Wen

Zhong

et al. 2022

Journal of Healthcare Engineering

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Traditional Chinese Medicine (TCM) is one of the oldest medical systems in the world, and inquiry is an essential part of TCM diagnosis. The development of artificial intelligence has led to the proposal of several computational TCM diagnostic methods. However, there are few research studies among them, and they have the following flaws: (1) insufficient engagement with the patient, (2) barren TCM consultation philosophy, and (3) inadequate validation of the method. As TCM inquiry knowledge is abstract and there are few relevant datasets, we devise a novel knowledge representation technique. The mapping of symptoms and syndromes is constructed based on the diagnostics of traditional Chinese medicine. As a guide, the inquiry knowledge base is constructed utilizing the “Ten Brief Inquiries,” TCM’s domain knowledge. Subsequently, a corresponding assessment approach is proposed for an intelligent consultation model for syndrome differentiation. We establish three criteria: the quality of the generated question-answer pairs, the accuracy of model identification, and the average number of questions. Three TCM specialists are asked to undertake a manual evaluation of the model separately. The results reveal that our approach is capable of pretty accurate syndrome differentiation. Furthermore, the model’s question and answer pairs for simulated consultations are relevant, accurate, and efficient.

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Recommending prescription via tongue image to assist clinician

Cited by 11 publications

References 47 publications

CATDSNet: Computer Aided Tongue Diagnosis System for Disease Prediction Using Hybrid Extreme Learning Machine

CATDSNet: Computer Aided Tongue Diagnosis System for Disease Prediction Using Hybrid Extreme Learning Machine

TCMPR: TCM Prescription Recommendation Based on Subnetwork Term Mapping and Deep Learning

Personalized Intelligent Syndrome Differentiation Guided By TCM Consultation Philosophy

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