Meta-path guided graph attention network for explainable herb recommendation

Jin, Yuanyuan; Ji, Wendi; Shi, Yao; Wang, Xiaoling; Yang, Xiaochun

doi:10.1007/s13755-022-00207-6

Cited by 15 publications

(9 citation statements)

References 57 publications

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“…In order to verify whether is effective the processing of data imbalance, also.KDHR: KDHR (Yang et al. , 2022) is the most comprehensive herbal medicines recommendation based on KG and GCN, and has achieved well.MGAT: MGAT (Jin et al. , 2023) is the latest method and tries to interpret the TCM prescription prediction through the meta-path KG.ML-TCM: On the basis of KDHR, the method of processing data imbalance is added.…”

Section: Methodsmentioning

confidence: 99%

“…Based on the idea of integrated learning, a multi-layer information fusion graph convolution approach (KDHR) generates symptom and herbs’ feature representation with rich information and low noise (Yang, Rao, Yu, & Kang, 2022). A meta-path-guided graph attention network tried to provide interpretable herb recommendations (Jin, Ji, Shi, Wang, & Yang, 2023). There is a phenomenon of label imbalance in the TCM dataset.…”

Section: Related Workmentioning

confidence: 99%

“…There are few existing methods to solve this problem, only tried reweighting. Works (Jin et al, 2020(Jin et al, , 2023 using the frequency of label (herb) occurrence as a weight to add to the mean squared error (MSE) loss function was proposed to overcome the label imbalance, but no major progress has been made.…”

Section: Related Workmentioning

confidence: 99%

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Prediction of traditional Chinese medicine prescriptions based on multi-label resampling

Jiang,

Wang,

Liu

et al. 2023

JEBDE

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PurposeTraditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.Design/methodology/approachIn this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.FindingsThe experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.Originality/valueThe authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Prediction of traditional Chinese medicine prescriptions based on multi-label resampling

Jiang,

Wang,

Liu

et al. 2023

JEBDE

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“…With knowledge graphs, explanations are easy to extract [135], [136] because of the rich semantic relations inherent in the representation. This kind of explainaibility is especially popular in applications such as product recommender systems (e.g., [19], [137], [138]) drug recommendations [139], [140]) and disease diagnosis [141], [142]).…”

Section: Knowledge-informed Explainability Methodsmentioning

confidence: 99%

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

Mumuni

2022

Prog Artif Intell

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We review current and emerging knowledge-informed and brain-inspired cognitive systems for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or few-shot learning. Data-driven deep learning models have achieved remarkable performance and demonstrated capabilities surpassing human experts in many applications. Yet, their inability to exploit domain knowledge leads to serious performance limitations in practical applications. In particular, deep learning systems are exposed to adversarial attacks, which can trick them into making glaringly incorrect decisions. Moreover, complex data-driven models typically lack interpretability or explainability, i.e., their decisions cannot be understood by human subjects. Furthermore, models are usually trained on standard datasets with a closed-world assumption. Hence, they struggle to generalize to unseen cases during inference in practical open-world environments, thus, raising the zero-or few-shot generalization problem. Although many conventional solutions exist, explicit domain knowledge, brain-inspired neural network and cognitive architectures offer powerful new dimensions towards alleviating these problems. Prior knowledge is represented in appropriate forms and incorporated in deep learning frameworks to improve performance. Brain-inspired cognition methods use computational models that mimic the human mind to enhance intelligent behavior in artificial agents and autonomous robots. Ultimately, these models achieve better explainability, higher adversarial robustness and data-efficient learning, and can, in turn, provide insights for cognitive science and neurosciencethat is, to deepen human understanding on how the brain works in general, and how it handles these problems.Index Terms-Domain knowledge, cognitive architecture, brain-inspired neural network, explainable AI, adversarial attack, zero-shot generalization. ✦ INTRODUCTION State of deep learningMachine learning is an artificial intelligence (AI) concept that enables computing systems to learn useful relationships from data and then use this information to identify learned patterns and make predictions on new inputs. Deep learning (DL) is a machine learning method that uses a multi-layered arrangement of computational units to learn relevant patterns from data. Many deep learning constructs have been proposed for various use-cases. Some of the most popular ones are deep convolutional neural networks (DCNN) [1], [2] recurrent neural networks (RNN) [3], generative adversarial networks (GANs) [4], deep reinforcement learning [5] and vision transformers [6], [7]). DCNNs are a class of deep neural networks (DNNs) specially designed to handle image data.Recently, deep learning methods have accomplished remarkable milestones in many critical applications. In medical domains, some state-of-the-art models (e.g., as reported

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“…Additionally, GNN can effectively capture structural and semantic information between entities and is widely applied to prescription generation tasks. Many studies[25, 26, 27, 28, 29] have utilized GNN to capture high-order correlations among herbs, symptoms, and prescriptions, recommending highquality herb collections based on background knowledge. However, existing methods overlooked internal prescription information such as compatibility principles and inherent properties of medicinal materials, while also lacking the use of mechanistic information like pathways and targets.…”

Section: Introductionmentioning

confidence: 99%

TCMM: A Unified Database for Traditional Chinese Medicine Modernization and Therapeutic Innovations

Ren,

et al. 2024

Preprint

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Mining the potential of traditional Chinese medicine (TCM) in treating modern diseases requires a profound understanding of its action mechanism and a comprehensive knowledge system that seamlessly bridges modern medical insights with traditional theories. However, existing databases for modernizing TCM are plagued by varying degrees of information loss, which impede the multidimensional dissection of pharmacological effects. To address this challenge, we introduce traditional Chinese medicine modernization (TCMM), the currently largest modernized TCM database that integrates pioneering intelligent pipelines. By aligning high-quality TCM and Western medicine data, TCMM boasts the most extensive TCM modernization knowledge, including 20 types of modernized TCM concepts such as prescription, ingredient, target and 46 biological relations among them, totaling 3,447,023 records. We demonstrate the efficacy and reliability of TCMM with two features, prescription generation and knowledge discovery, the outcomes show consistency with biological experimental results. A publicly available web interface is athttps://www.tcmm.net.cn/.

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Meta-path guided graph attention network for explainable herb recommendation

Cited by 15 publications

References 57 publications

Prediction of traditional Chinese medicine prescriptions based on multi-label resampling

Prediction of traditional Chinese medicine prescriptions based on multi-label resampling

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

TCMM: A Unified Database for Traditional Chinese Medicine Modernization and Therapeutic Innovations

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