Use of SNOMED CT in Large Language Models: Scoping Review.

BACKGROUND: Large language models (LLMs) have substantially advanced natural language processing (NLP) capabilities but often struggle with knowledge-driven tasks in specialized domains such as biomedicine. Integrating biomedical knowledge sources such as SNOMED CT into LLMs may enhance their performance on biomedical tasks. However, the methodologies and effectiveness of incorporating SNOMED CT into LLMs have not been systematically reviewed. OBJECTIVE: This scoping review aims to examine how SNOMED CT is integrated into LLMs, focusing on (1) the types and components of LLMs being integrated with SNOMED CT, (2) which contents of SNOMED CT are being integrated, and (3) whether this integration improves LLM performance on NLP tasks. METHODS: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we searched ACM Digital Library, ACL Anthology, IEEE Xplore, PubMed, and Embase for relevant studies published from 2018 to 2023. Studies were included if they incorporated SNOMED CT into LLM pipelines for natural language understanding or generation tasks. Data on LLM types, SNOMED CT integration methods, end tasks, and performance metrics were extracted and synthesized. RESULTS: The review included 37 studies. Bidirectional Encoder Representations from Transformers and its biomedical variants were the most commonly used LLMs. Three main approaches for integrating SNOMED CT were identified: (1) incorporating SNOMED CT into LLM inputs (28/37, 76%), primarily using concept descriptions to expand training corpora; (2) integrating SNOMED CT into additional fusion modules (5/37, 14%); and (3) using SNOMED CT as an external knowledge retriever during inference (5/37, 14%). The most frequent end task was medical concept normalization (15/37, 41%), followed by entity extraction or typing and classification. While most studies (17/19, 89%) reported performance improvements after SNOMED CT integration, only a small fraction (19/37, 51%) provided direct comparisons. The reported gains varied widely across different metrics and tasks, ranging from 0.87% to 131.66%. However, some studies showed either no improvement or a decline in certain performance metrics. CONCLUSIONS: This review demonstrates diverse approaches for integrating SNOMED CT into LLMs, with a focus on using concept descriptions to enhance biomedical language understanding and generation. While the results suggest potential benefits of SNOMED CT integration, the lack of standardized evaluation methods and comprehensive performance reporting hinders definitive conclusions about its effectiveness. Future research should prioritize consistent reporting of performance comparisons and explore more sophisticated methods for incorporating SNOMED CT's relational structure into LLMs. In addition, the biomedical NLP community should develop standardized evaluation frameworks to better assess the impact of ontology integration on LLM performance.

Enhanced knowledge graph recommendation algorithm based on multi-level contrastive learning.

Integrating the Knowledge Graphs (KGs) into recommendation systems enhances personalization and accuracy. However, the long-tail distribution of knowledge graphs often leads to data sparsity, which limits the effectiveness in practical applications. To address this challenge, this study proposes a knowledge-aware recommendation algorithm framework that incorporates multi-level contrastive learning. This framework enhances the Collaborative Knowledge Graph (CKG) through a random edge dropout method, which constructs feature representations at three levels: user-user interactions, item-item interactions and user-item interactions. A dynamic attention mechanism is employed in the Graph Attention Networks (GAT) for modeling the KG. Combined with the nonlinear transformation and Momentum Contrast (Moco) strategy for contrastive learning, it can effectively extract high-quality feature information. Additionally, multi-level contrastive learning, as an auxiliary self-supervised task, is jointly trained with the primary supervised task, which further enhances recommendation performance. Experimental results on the MovieLens and Amazon-books datasets demonstrate that this framework effectively improves the performance of knowledge graph-based recommendations, addresses the issue of data sparsity, and outperforms other baseline models across multiple evaluation metrics.

Interaction-knowledge semantic alignment for recommendation.

In order to alleviate the issue of data sparsity, knowledge graphs are introduced into recommender systems because they contain diverse information about items. The existing knowledge graph enhanced recommender systems utilize both user-item interaction data and knowledge graph, but those methods ignore the semantic difference between interaction data and knowledge graph. On the other hand, for the item representations obtained from two kinds of graph structure data respectively, the existing methods of fusing representations only consider the item representations themselves, without considering the personalized preference of users. In order to overcome the limitations mentioned above, we present a recommendation method named Interaction-Knowledge Semantic Alignment for Recommendation (IKSAR). By introducing a semantic alignment module, the semantic difference between the interaction bipartite graph and the knowledge graph is reduced. The representation of user is integrated during the fusion of representations of item, which improves the quality of the fused representation of item. To validate the efficacy of the proposed approach, we perform comprehensive experiments on three datasets. The experimental results demonstrate that the IKSAR is superior to the existing methods, showcasing notable improvement.

Enhancing ophthalmology medical record management with multi-modal knowledge graphs.

The electronic medical record management system plays a crucial role in clinical practice, optimizing the recording and management of healthcare data. To enhance the functionality of the medical record management system, this paper develops a customized schema designed for ophthalmic diseases. A multi-modal knowledge graph is constructed, which is built upon expert-reviewed and de-identified real-world ophthalmology medical data. Based on this data, we propose an auxiliary diagnostic model based on a contrastive graph attention network (CGAT-ADM), which uses the patient's diagnostic results as anchor points and achieves auxiliary medical record diagnosis services through graph clustering. By implementing contrastive methods and feature fusion of node types, text, and numerical information in medical records, the CGAT-ADM model achieved an average precision of 0.8563 for the top 20 similar case retrievals, indicating high performance in identifying analogous diagnoses. Our research findings suggest that medical record management systems underpinned by multimodal knowledge graphs significantly enhance the development of AI services. These systems offer a range of benefits, from facilitating assisted diagnosis and addressing similar patient inquiries to delving into potential case connections and disease patterns. This comprehensive approach empowers healthcare professionals to garner deeper insights and make well-informed decisions.

Combining large language models with enterprise knowledge graphs: a perspective on enhanced natural language understanding.

Knowledge Graphs (KGs) have revolutionized knowledge representation, enabling a graph-structured framework where entities and their interrelations are systematically organized. Since their inception, KGs have significantly enhanced various knowledge-aware applications, including recommendation systems and question-answering systems. Sensigrafo, an enterprise KG developed by Expert.AI, exemplifies this advancement by focusing on Natural Language Understanding through a machine-oriented lexicon representation. Despite the progress, maintaining and enriching KGs remains a challenge, often requiring manual efforts. Recent developments in Large Language Models (LLMs) offer promising solutions for KG enrichment (KGE) by leveraging their ability to understand natural language. In this article, we discuss the state-of-the-art LLM-based techniques for KGE and show the challenges associated with automating and deploying these processes in an industrial setup. We then propose our perspective on overcoming problems associated with data quality and scarcity, economic viability, privacy issues, language evolution, and the need to automate the KGE process while maintaining high accuracy.

Data-Driven Strain Sensor Design Based on a Knowledge Graph Framework.

Wearable flexible strain sensors require different performance depending on the application scenario. However, developing strain sensors based solely on experiments is time-consuming and often produces suboptimal results. This study utilized sensor knowledge to reduce knowledge redundancy and explore designs. A framework combining knowledge graphs and graph representational learning methods was proposed to identify targeted performance, decipher hidden information, and discover new designs. Unlike process-parameter-based machine learning methods, it used the relationship as semantic features to improve prediction precision (up to 0.81). Based on the proposed framework, a strain sensor was designed and tested, demonstrating a wide strain range (300%) and closely matching predicted performance. This predicted sensor performance outperforms similar materials. Overall, the present work is favorable to design constraints and paves the way for the long-awaited implementation of text-mining-based knowledge management for sensor systems, which will facilitate the intelligent sensor design process.

BGAT-CCRF: A novel end-to-end model for knowledge graph noise correction.

Knowledge graph (KG) noise correction aims to select suitable candidates to correct the noises in KGs. Most of the existing studies have limited performance in repairing the noisy triple that contains more than one incorrect entity or relation, which significantly constrains their implementation in real-world KGs. To overcome this challenge, we propose a novel end-to-end model (BGAT-CCRF) that achieves better noise correction results. Specifically, we construct a balanced-based graph attention model (BGAT) to learn the features of nodes in triples' neighborhoods and capture the correlation between nodes based on their position and frequency. Additionally, we design a constrained conditional random field model (CCRF) to select suitable candidates guided by three constraints for correcting one or more noises in the triple. In this way, BGAT-CCRF can select multiple candidates from a smaller domain to repair multiple noises in triples simultaneously, rather than selecting candidates from the whole KG to repair noisy triples as traditional methods do, which can only repair one noise in the triple at a time. The effectiveness of BGAT-CCRF is validated by the KG noise correction experiment. Compared with the state-of-the-art models, BGAT-CCRF improves the fMRR metric by 3.58% on the FB15K dataset. Hence, it has the potential to facilitate the implementation of KGs in the real world.

Integration of Diffusion Transformer and Knowledge Graph for Efficient Cucumber Disease Detection in Agriculture.

In this study, a deep learning method combining knowledge graph and diffusion Transformer has been proposed for cucumber disease detection. By incorporating the diffusion attention mechanism and diffusion loss function, the research aims to enhance the model's ability to recognize complex agricultural disease features and to address the issue of sample imbalance efficiently. Experimental results demonstrate that the proposed method outperforms existing deep learning models in cucumber disease detection tasks. Specifically, the method achieved a precision of 93%, a recall of 89%, an accuracy of 92%, and a mean average precision (mAP) of 91%, with a frame rate of 57 frames per second (FPS). Additionally, the study successfully implemented model lightweighting, enabling effective operation on mobile devices, which supports rapid on-site diagnosis of cucumber diseases. The research not only optimizes the performance of cucumber disease detection, but also opens new possibilities for the application of deep learning in the field of agricultural disease detection.

A feature-enhanced knowledge graph neural network for machine learning method recommendation.

Large amounts of machine learning methods with condensed names bring great challenges for researchers to select a suitable approach for a target dataset in the area of academic research. Although the graph neural networks based on the knowledge graph have been proven helpful in recommending a machine learning method for a given dataset, the issues of inadequate entity representation and over-smoothing of embeddings still need to be addressed. This article proposes a recommendation framework that integrates the feature-enhanced graph neural network and an anti-smoothing aggregation network. In the proposed framework, in addition to utilizing the textual description information of the target entities, each node is enhanced through its neighborhood information before participating in the higher-order propagation process. In addition, an anti-smoothing aggregation network is designed to reduce the influence of central nodes in each information aggregation by an exponential decay function. Extensive experiments on the public dataset demonstrate that the proposed approach exhibits substantial advantages over the strong baselines in recommendation tasks.

Knowledge graph confidence-aware embedding for recommendation.

Knowledge graphs (KG) are vital for extracting and storing knowledge from large datasets. Current research favors knowledge graph-based recommendation methods, but they often overlook the features learning of relations between entities and focus excessively on entity-level details. Moreover, they ignore a crucial fact: the aggregation process of entity and relation features in KG is complex, diverse, and imbalanced. To address this, we propose a recommendation-oriented KG confidence-aware embedding technique. It introduces an information aggregation graph and a confidence feature aggregation mechanism to overcome these challenges. Additionally, we quantify entity confidence at the feature and category levels, improving the precision of embeddings during information propagation and aggregation. Our approach achieves significant improvements over state-of-the-art KG embedding-based recommendation methods, with up to 6.20% increase in AUC and 8.46% increase in GAUC, as demonstrated on four public KG datasets2.

Knowledge-reinforced explainable next basket recommendation.

The next basket recommendation task aims to predict the items in the user's next basket by modeling the user's basket sequence. Existing next basket recommendations focus on improving recommendation performance, and most of these methods are black-box models, ignoring the importance of providing explanations to improve user satisfaction. Furthermore, most next basket recommendation methods are designed for consumer users, and few methods are proposed for business user characteristics. To address the above problems, we propose a Knowledge Reinforced Explainable Next Basket Recommendation (KRE-NBR). Specifically, we construct a basket-based knowledge graph and obtain pretrained embeddings of entities that contain rich information of the knowledge graph. To obtain high-quality user predictive vectors, we fuse user pretrained embeddings, user basket sequence level embeddings, and user repurchase embeddings. One highlight of the user repurchase embeddings is that they are able to model business user repurchase behavior. To make the results of next basket recommendations explainable, we use reinforcement learning for path reasoning to find the items recommended in the next basket and generate recommendation explanations at the same time. To the best of our knowledge, this is the first work to provide recommendation explanations for next basket recommendations. Extensive experiments on real datasets show that the recommendation performance of our proposed approach outperforms several state-of-the-art baselines.

Tree hole rescue: an AI approach for suicide risk detection and online suicide intervention.

Adolescent suicide has become an important social issue of general concern. Many young people express their suicidal feelings and intentions through online social media, e.g., Twitter, Microblog. The "tree hole" is the Chinese name for places on the Web where people post secrets. It provides the possibility of using Artificial Intelligence and big data technology to detect the posts where someone express the suicidal signal from those "tree hole" social media. We have developed the Web-based intelligent agents (i.e., AI-based programs) which can monitor the "tree hole" websites in Microblog every day by using knowledge graph technology. We have organized Tree-hole Rescue Team, which consists of more than 1000 volunteers, to carry out suicide rescue intervention according to the daily monitoring notifications. From 2018 to 2023, Tree-hole Rescue Team has prevented more than 6600 suicides. A few thousands of people have been saved within those 6 years. In this paper, we present the basic technology of Web-based Tree Hole intelligent agents and elaborate how the intelligent agents can discover suicide attempts and issue corresponding monitoring notifications and how the volunteers of Tree Hole Rescue Team can conduct online suicide intervention. This research also shows that the knowledge graph approach can be used for the semantic analysis on social media.

FuseLinker: Leveraging LLM's pre-trained text embeddings and domain knowledge to enhance GNN-based link prediction on biomedical knowledge graphs.

OBJECTIVE: To develop the FuseLinker, a novel link prediction framework for biomedical knowledge graphs (BKGs), which fully exploits the graph's structural, textual and domain knowledge information. We evaluated the utility of FuseLinker in the graph-based drug repurposing task through detailed case studies. METHODS: FuseLinker leverages fused pre-trained text embedding and domain knowledge embedding to enhance the graph neural network (GNN)-based link prediction model tailored for BKGs. This framework includes three parts: a) obtain text embeddings for BKGs using embedding-visible large language models (LLMs), b) learn the representations of medical ontology as domain knowledge information by employing the Poincaré graph embedding method, and c) fuse these embeddings and further learn the graph structure representations of BKGs by applying a GNN-based link prediction model. We evaluated FuseLinker against traditional knowledge graph embedding models and a conventional GNN-based link prediction model across four public BKG datasets. Additionally, we examined the impact of using different embedding-visible LLMs on FuseLinker's performance. Finally, we investigated FuseLinker's ability to generate medical hypotheses through two drug repurposing case studies for Sorafenib and Parkinson's disease. RESULTS: By comparing FuseLinker with baseline models on four BKGs, our method demonstrates superior performance. The Mean Reciprocal Rank (MRR) and Area Under receiver operating characteristic Curve (AUROC) for KEGG50k, Hetionet, SuppKG and ADInt are 0.969 and 0.987, 0.548 and 0.903, 0.739 and 0.928, and 0.831 and 0.890, respectively. CONCLUSION: Our study demonstrates that FuseLinker is an effective novel link prediction framework that integrates multiple graph information and shows significant potential for practical applications in biomedical and clinical tasks. Source code and data are available at https://github.com/YKXia0/FuseLinker.

Research Progresses and Applications of Knowledge Graph Embedding Technique in Chemistry.

A knowledge graph (KG) is a technique for modeling entities and their interrelations. Knowledge graph embedding (KGE) translates these entities and relationships into a continuous vector space to facilitate dense and efficient representations. In the domain of chemistry, applying KG and KGE techniques integrates heterogeneous chemical information into a coherent and user-friendly framework, enhances the representation of chemical data features, and is beneficial for downstream tasks, such as chemical property prediction. This paper begins with a comprehensive review of classical and contemporary KGE methodologies, including distance-based models, semantic matching models, and neural network-based approaches. We then catalogue the primary databases employed in chemistry and biochemistry that furnish the KGs with essential chemical data. Subsequently, we explore the latest applications of KG and KGE in chemistry, focusing on risk assessment, property prediction, and drug discovery. Finally, we discuss the current challenges to KG and KGE techniques and provide a perspective on their potential future developments.

Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics.

The emergence of perturbation transcriptomics provides a new perspective for drug discovery, but existing analysis methods suffer from inadequate performance and limited applicability. In this work, we present PertKGE, a method designed to deconvolute compound-protein interactions from perturbation transcriptomics with knowledge graph embedding. By considering multi-level regulatory events within biological systems that share the same semantic context, PertKGE significantly improves deconvoluting accuracy in two critical "cold-start" settings: inferring targets for new compounds and conducting virtual screening for new targets. We further demonstrate the pivotal role of incorporating multi-level regulatory events in alleviating representational biases. Notably, it enables the identification of ectonucleotide pyrophosphatase/phosphodiesterase-1 as the target responsible for the unique anti-tumor immunotherapy effect of tankyrase inhibitor K-756 and the discovery of five novel hits targeting the emerging cancer therapeutic target aldehyde dehydrogenase 1B1 with a remarkable hit rate of 10.2%. These findings highlight the potential of PertKGE to accelerate drug discovery.

Predicting drug-target interactions by measuring confidence with consistent causal neighborhood interventions.

Predicting drug-target interactions (DTI) is a crucial stage in drug discovery and development. Understanding the interaction between drugs and targets is essential for pinpointing the specific relationship between drug molecules and targets, akin to solving a link prediction problem using information technology. While knowledge graph (KG) and knowledge graph embedding (KGE) methods have been rapid advancements and demonstrated impressive performance in drug discovery, they often lack authenticity and accuracy in identifying DTI. This leads to increased misjudgment rates and reduced efficiency in drug development. To address these challenges, our focus lies in refining the accuracy of DTI prediction models through KGE, with a specific emphasis on causal intervention confidence measures (CI). These measures aim to assess triplet scores, enhancing the precision of the predictions. Comparative experiments conducted on three datasets and utilizing 9 KGE models reveal that our proposed confidence measure approach via causal intervention, significantly improves the accuracy of DTI link prediction compared to traditional approaches. Furthermore, our experimental analysis delves deeper into the embedding of intervention values, offering valuable insights for guiding the design and development of subsequent drug development experiments. As a result, our predicted outcomes serve as valuable guidance in the pursuit of more efficient drug development processes.

Knowledge mapping and bibliometric analysis of medical knee magnetic resonance imaging for knee osteoarthritis (2004-2023).

Objectives: Magnetic resonance imaging (MRI) is increasingly used to detect knee osteoarthritis (KOA). In this study, we aimed to systematically examine the global research status on the application of medical knee MRI in the treatment of KOA, analyze research hotspots, explore future trends, and present results in the form of a knowledge graph. Methods: The Web of Science core database was searched for studies on medical knee MRI scans in patients with KOA between 2004 and 2023. CiteSpace, SCImago Graphica, and VOSviewer were used for the country, institution, journal, author, reference, and keyword analyses. Results: A total of 2,904 articles were included. The United States and Europe are leading countries. Boston University is the main institution. Osteoarthritis and cartilage is the main magazine. The most frequently cocited article was "Radiological assessment of osteoarthrosis". Guermazi A was the author with the highest number of publications and total references. The keywords most closely linked to MRI and KOA were "cartilage", "pain", and "injury". Conclusions: The application of medical knee MRI in KOA can be divided into the following parts: (1). MRI was used to assess the relationship between the characteristics of local tissue damage and pathological changes and clinical symptoms. (2).The risk factors of KOA were analyzed by MRI to determine the early diagnosis of KOA. (3). MRI was used to evaluate the efficacy of multiple interventions for KOA tissue damage (e.g., cartilage defects, bone marrow edema, bone marrow microfracture, and subchondral bone remodeling). Artificial intelligence, particularly deep learning, has become the focus of research on MRI applications for KOA.

Knowledge graph based reasoning in medical image analysis: A scoping review.

Automated computer-aided diagnosis (CAD) is becoming more significant in the field of medicine due to advancements in computer hardware performance and the progress of artificial intelligence. The knowledge graph is a structure for visually representing knowledge facts. In the last decade, a large body of work based on knowledge graphs has effectively improved the organization and interpretability of large-scale complex knowledge. Introducing knowledge graph inference into CAD is a research direction with significant potential. In this review, we briefly review the basic principles and application methods of knowledge graphs firstly. Then, we systematically organize and analyze the research and application of knowledge graphs in medical imaging-assisted diagnosis. We also summarize the shortcomings of the current research, such as medical data barriers and deficiencies, low utilization of multimodal information, and weak interpretability. Finally, we propose future research directions with possibilities and potentials to address the shortcomings of current approaches.

Data-driven drug treatment: enhancing clinical decision-making with SalpPSO-optimized GraphSAGE.

Safe drug recommendation systems play a crucial role in minimizing adverse drug reactions and enhancing patient safety. In this research, we propose an innovative approach to develop a safety drug recommendation system by integrating the Salp Swarm Optimization-based Particle Swarm Optimization (SalpPSO) with the GraphSAGE algorithm. The goal is to optimize the hyper parameters of GraphSAGE, enabling more accurate drug-drug interaction prediction and personalized drug recommendations. The research begins with data collection from real-world datasets, including MIMIC-III, Drug Bank, and ICD-9 ontology. The databases provide comprehensive and diverse clinical data related to patients, diseases, and drugs, forming the foundation of a knowledge graph. It represents drug-related entities and their relationships, such as drugs, indications, adverse effects, and drug-drug interactions. The knowledge graph's integration of patient data, disease ontology, and drug information enhances the system's accuracy to predict drug-drug interactions as well as identifying potential detrimental drug reactions. The GraphSAGE algorithm is employed as the base model for learning node embeddings in the knowledge graph. To enhance its performance, we propose the SalpPSO algorithm for hyper parameter optimization. SalpPSO combines features from Salp Swarm Optimization and Particle Swarm Optimization, offering a robust and effective optimization process. The optimized hyper parameters lead to more reliable and accurate drug recommendation system. For evaluation, the dataset is split into training and validation sets and compared the performance of the modified GraphSAGE model with SalpPSO-optimized hyper parameters to the standard models. The experimental analysis conducted in terms of various measures proves the efficiency of the proposed safe recommendation system, offering valuable for healthcare experts in making more informed and personalized drug treatment decisions for patients.

Label Transfer for Drug Disease Association in Three Meta-Paths.

The identification of potential interactions and relationships between diseases and drugs is significant in public health care and drug discovery. As we all know, experimenting to determine the drug-disease interactions is very expensive in both time and money. However, there are still many drug-disease associations that are still undiscovered and potential. Therefore, the development of computational methods to explore the relationship between drugs and diseases is very important and essential. Many computational methods for predicting drug-disease associations have been developed based on known interactions to learn potential interactions of unknown drug-disease pairs. In this paper, we propose 3 new main groups of meta-paths based on the heterogeneous biological network of drug-protein-disease objects. For each meta-path, we design a machine learning model, then an integrated learning method is formed by these models. We evaluated our approach on 3 standard datasets which are DrugBank, OMIM, and Gottlieb's dataset. Experimental results demonstrate that the proposed method is better than some recent methods such as EMP-SVD, LRSSL, MBiRW, MPG-DDA, SCMFDD,. . . in some measures such as AUC, AUPR, and F1-score.