Biomedical Information Retrieval (BIR) aims to identify relevant evidence from large-scale biomedical literature and knowledge resources in response to user information needs. In practical settings, biomedical queries are often underspecified and fail to express critical disease, molecular, or mechanistic context, leading to a semantic gap between user queries and relevant documents ultimately limiting retrieval effectiveness. Query expansion is a common strategy for alleviating this mismatch. Recent large language model (LLM)-based query expansion methods have demonstrated the ability to generate contextually relevant terms by leveraging parametric knowledge. However, such methods generally lack explicit modeling of core biomedical concepts and often expand queries without sufficiently grounding generated terms in retrieval evidence. This limitation can introduce noisy or weakly supported biomedical terms, thereby reducing retrieval precision and robustness. To address this issue, we propose Concept-Centric Reasoning (CCR), an evidence-guided query expansion framework that treats biomedical query expansion as an iterative concept reasoning process. Instead of directly generating expanded terms, CCR first identifies concept-level evidence from retrieved documents, including disease entities, clinical phenotypes, biological processes, and pathological mechanisms, and then progressively refines the query through alternating retrieval and concept reasoning steps. To further improve expansion quality, CCR incorporates a concept reflection stage that consolidates and re-evaluates inferred concepts according to their consistency across retrieved evidence, producing a more grounded and informative expanded query. Experiments on TREC-COVID and NFCorpus show that CCR consistently improves over BM25 under the same retrieval backbone and remains competitive with recent LLM-based query expansion methods, particularly on TREC-COVID and top-ranked precision metrics.

Multimodal aspect-based sentiment classification (MABSC) aims to identify the sentiment polarity toward specific aspects in multimodal data. It has gained significant attention with the increasing use of social media platforms. Existing approaches primarily focus on analyzing the content of posts to predict sentiment. However, they often struggle with limited contextual information inherent in social media posts, hindering accurate sentiment detection. To overcome this issue, we propose a novel multimodal dual cause analysis (MDCA) method to track the underlying causes behind expressed sentiments. MDCA can provide additional reasoning cause (RC) and direct cause (DC) to explain why users express certain emotions, thus helping improve the accuracy of sentiment prediction. To develop a model with MDCA, we construct MABSC datasets with RC and DC by utilizing large language models (LLMs) and visual-language models. Subsequently, we devise a multitask learning framework that leverages the datasets with cause data to train a small generative model, which can generate RC and DC, and predict the sentiment assisted by these causes. Experimental results on MABSC benchmark datasets demonstrate that our MDCA model achieves the state-of-the-art performance, and the small fine-tuned model exhibits superior adaptability to MABSC compared to large models like ChatGPT and BLIP-2.

Few-shot multi-intent spoken language understanding (SLU) aims to identify users’ multiple intents and key slots using a tiny amount of annotated data. Recent advances in large language models (LLMs) have utilized instruction learning frameworks to model intent-slot interdependencies, typically requiring abundant data for effective training. However, in few-shot scenarios, these frameworks face challenges such as mismatches between the number of generated slots and input lengths, relational confusion in multi-intent scenarios and neglect of task-specific variations in intent counts across utterances. To overcome the challenges, we propose PICD-Instruct, a novel generative framework based on Basic Instructions (BI), Pairwise Interaction Instructions (PII) and Contrastive Distinct Instructions (CDI). Specifically, BI directs LLMs to generate entities along with associated words, thereby mitigating mismatches in quantitative correspondences. PII explicitly captures dual-task interdependencies by guiding LLMs to pair each intent with its related entities. CDI enhances understanding of utterances by guiding LLMs to determine whether two utterances share the same intent count. Experimental results on public datasets indicate that PICD-Instruct achieves state-of-the-art performance.

Concept-based approaches have emerged as a promising solution to enhance search accuracy in biomedical information retrieval (BIR). These approaches aim to bridge the semantic gap between user queries and document content by mapping both to a shared conceptual space. However, existing methods often struggle with limited concept coverage and inaccurate concept mapping. Recent advancements in large language models (LLMs) have demonstrated their extensive concept coverage and ability to accurately connect text with relevant concepts, showing potential in addressing these challenges. Motivated by these findings, we propose Concept-based Biomedical Text Expansion (CBTE), a novel method that leverages LLMs to identify relevant concepts in both queries and documents. CBTE then uses these concepts to expand queries and documents. The relevance between expanded queries and documents is evaluated using the BM25 sparse retrieval framework. To validate CBTE's effectiveness, we conducted comprehensive experiments on two well-known biomedical datasets, NFCorpus and Trec-Covid. The results indicate that CBTE significantly outperforms existing baselines.

Few-shot multi-intent spoken language understanding (SLU) aims to detect user’s multiple intents and key slots using a tiny amount of annotated data. Prevailing multi-intent SLU models typically rely on abundant data for effective training, enabling them to capture corresponding relationships between intents and slots. However, in few-shot scenarios, establishing these connections becomes challenging, especially in situations involving multiple intents, which may result in confused relationships between intents and slots. To overcome the challenge, we propose decomposing the multi-label intent detection task into several single-label tasks, which reduces the complexity of model training while preserving the constraint relationships between intents and their related slots. We design description templates for each intent and respectively predict the correlation between the utterance and each intent description while completing the slot filling task under the corresponding intent. Therefore, each intent can independently guide the slot filling process, mitigating potentially confused relationships between multiple intents and slots. Experimental results on public datasets indicate that the performance of our model is better than ChatGPT and achieves state-of-the-art results.

Detecting drug side effects is crucial in development of drugs. As publicly available biomedical data expands, researchers have devised numerous computational methods for predicting drug-side effect associations (DSAs). Among these, network-based approaches have gained significant attention in the biomedical field. However, the challenge of data scarcity poses a significant hurdle for existing DSAs prediction models. While various data augmentation methods have been created to solve the proble, most rely on random alterations to the original networks, neglecting the causality of DSAs’ existence, thus impacting the predictive performance negatively. In this paper, we introduce a counterfactual inference-based data augmentation method to enhance performance. First,a heterogeneous information network (HIN) is construct by integrating multiple biomedical data sources. We employ community detection on the HIN to preform a counterfactual inference-based method, deriving augmented links and an augmented HIN. Subsequently, we apply a meta-path-based graph neural network to obtain high-quality representations of drugs and side effects, enabling the prediction of DSAs. Our comprehensive experiments confirm the effectiveness of this counterfactual inference-based data augmentation for DSAs prediction.

Side effects of drugs have gained increasing attention in the biomedical field, and accurate identification of drug side effects is essential for drug development and drug safety surveillance. Although the traditional pharmacological experiments can accurately detect the side effects of drugs, the identifying process is time-consuming, costly, and may lead to incomplete identification of side effects. With the expanding of various biomedical databases, many computational methods have been developed for the task of drug-side effect associations (DSAs) prediction. However, existing methods have the following three drawbacks: 1). multiple drug-related databases are not fully used; 2). the complex semantics among drugs and side effects are not effectively captured; 3). the explainability of the predicted DSAs is missed for most existing methods. Therefore, there is an urgent need to find a more effective method for predicting DSAs. To address these issues, we propose a novel meta-path-based graph neural network model for drug-side effect associations prediction (MPGNN-DSA). In MPGNN-DSA, a heterogeneous information network is first constructed by combining multiple biological datasets. Then, a meta-path-based feature learning module is utilized for learning high-quality representations of drugs and side effects by capturing the semantics contained in meta-paths of the constructed HIN. With the learned features, the prediction module is conducted to derive the predicted side effects for drugs. In addition, the explainability of the predicted DSAs can be provided as well with the semantics contained in meta-paths. We conduct comprehensive experiments, and the results demonstrate the effectiveness of MPGNN-DSA, suggesting that the proposed method will be a feasible solution to the task of DSAs prediction.

Drug repositioning is the process of identifying potential associations between approved drugs and diseases (DDAs) to unveil novel therapeutic applications. Unlike traditional drug discovery approaches, a key advantage of drug repositioning lies in its capacity to leverage the existing knowledge and safety profiles of established medications, leading to significant reductions in both the time and costs associated with drug development. While various methods have been proposed to address this challenge using diverse strategies, the conventional approach for training DDAs prediction models typically relies on random sampling of unknown drug-disease pairs to construct negative samples. However, this method may inadvertently introduce unwanted noise or errors by erroneously categorizing some genuine DDAs as negative samples, thereby leaving room for improvement in current methodologies. In this paper, we introduce a novel negative sample selection algorithm for DDAs prediction that explicitly incorporates causal knowledge inherent in DDAs. To accomplish this, we first construct a heterogeneous information network (HIN) that encompasses various biological entities associated with DDAs and their interconnections. Subsequently, we utilize the outcomes of community detection within the HIN as a form of counterfactual inference, resulting in the development of a negative sample selection algorithm based on a thoughtfully designed counterfactual question. By combining the known DDAs (i.e., positive samples) with the newly generated negative samples, we train a prediction model that incorporates a graph learning module to acquire representations of drugs and diseases. Comprehensive experiments confirm the effectiveness of our proposed model for DDAs prediction.

In few-shot settings, fully conveying the semantic information of the dialogue act is a crucial challenge for Natural Language Generation (NLG) in the task-oriented dialogue system. An interesting fact is that NLG and Spoken Language Understanding (SLU) are a natural dual problem pair. Suppose the response generated by the NLG module can be restored to the corresponding dialogue act by the SLU module, which reflects that the generated response fully conveys the semantic information of the dialogue act. Based on this idea, a novel Dual Supervised Pre-trained Model for a few-shot Natural Language Generation (DSPM-NLG) is proposed to regularize the pre-training process. We adopt a joint model with a dual supervised framework to learn the dual correlation between NLG and SLU from the perspective of probability. In addition, a slot-masked strategy is designed to enable the model to focus better on the key slot-value pairs. DSPM-NLG is continuously trained on existing public large-scale annotated data, which thoroughly learns the duality between two tasks to enhance the semantically controlling and generalization abilities of the pre-trained model. Experiments demonstrate that our proposed model performs outstandingly on the few-shot benchmark dataset and outperforms the previous SOTA results.

The established links between nutrition and human health are widely acknowledged. Dietary nutrients play a crucial role in regulating gut microbial communities, influencing various human diseases. With a growing number of related studies, there’s a need to systematically organize these associations for coherent knowledge reasoning. However, due to the diverse and extensive nature of the knowledge landscape, significant challenges persist. To address this, we propose an approach using multimodal data and knowledge embeddings for effective knowledge reasoning in nutrition and human health. We create a comprehensive knowledge graph, KG4NH, covering dietary nutrition, gut microbiota, and human diseases. To ensure efficient knowledge representation, we employ knowledge embedding techniques to develop modality-specific encoders for structure, category, and description. Additionally, we introduce a mul-timodal fusion method to capture shared information across modalities. Our experimental results demonstrate the superiority of our approach over other state-of-the-art methods.

Most previous few-shot Spoken Language Understanding (SLU) models typically need to be trained on a set of data-rich source domains and adapt to the target domain with a few examples. In this paper, we explore a more practical scenario for few-shot SLU, in which we only assume access to a pre-trained language model and a few labeled examples without any other source domain data. We concentrate on understanding how far the few-shot SLU could be pushed in this setting. To this end, we develop a prompt-based intent detection model in few-shot settings, which leverages the BERT original pre-training next sentence prediction task and the prompt template to detect the user’s intent. For slot filling, we propose an approach of reconstructing slot labels, which reduces the training complexity by reducing the number of slot labels in few-shot settings. To evaluate the few-shot SLU for a more practical scenario, we present two benchmarks, FewShotATIS and FewShotSNIPS. And a dynamic sampling strategy is designed to construct the two datasets according to the learning difficulty of each intent and slot. Experiments on FewShotATIS and FewShotSNIPS demonstrate that our proposed model achieves state-of-the-art performance.

This paper proposes a real-time measurement technology of digital speckle deformation based on Fourier transform by slightly improving the existing out-of-plane deformation measurement technology of planar objects. The speckle image collected by the CCD is subjected to carrier elimination, inverse Fourier transform and other methods to obtain the speckle interference fringes with low noise and high contrast. The experimental results of the out-of-plane deformation measurement of the aluminum plate show that the technology can measure the real-time deformation of the tested piece.

目的 建立手术室感染防控四级督导体系,确保手术室常态化疫情防控时期感染控制效果.方法 从手术人员、环境、流程方面分析手术室常态化疫情防控方面的不足之处,在原有的"护士长-护理部-医院感染科"三级督导体系基础上,建立手术室四级督导体系,增设一级专职感控督导岗位.根据条件选拔专职感控督导员,梳理工作职责,建立手术室日常感控督导流程,加强每日手术人员、环境、流程等方面的监督管理和评价,比较四级督导体系建立前后感染控制隐患发生例数、手术人员防护穿脱合格率、手术间高频接触物表处理合格率、手术患者接诊流程合格率.结果 感染控制隐患发生例数明显降低、手术人员防护穿脱合格率由37％提升至98％,手术间高频接触物表处理合格率由34％提升至76％,手术患者接诊流程合格率由46.7％提升至99.3％(P<0.001).结论 构建手术室四级常态化疫情感控督导体系,健全常态化疫情防控时期感控组织管理架构,一级专职感控督导岗位落实常态化疫情防控日常督导工作,确保疫情防控效果,保障患者和医护人员的安全.

当前,我国高等院校正在积极推进混合式教学,以改进教学质量.然而,由于教师的网络教学投入没有得到应有的认可,教师开展混合式教学的积极性不高,混合式教学的实施现状并不乐观.为此,文章深入分析了网络教学投入的内涵和结构要素,在此基础上设计了基于教学行为分析的高校教师网络教学投入评价指标,并使用层次分析法确定了各项指标的权重.之后,文章将基于教学行为分析的高校教师网络教学投入评价方法在湖北省Z大学进行了具体应用,结果显示:教师的精力投入水平相对较高,而认知、情感投入水平相对较低;学校管理部门的负责人认为此方法相对于传统的专家打分法更为高效、客观.文章的研究可用于指导高校开展网络教学投入评价实践,有助于提升教师开展混合式教学的积极性,进而提升高等教育的教学质量.

Over the past decades, Chemical-induced Disease (CID) relations have attracted extensive attention in biomedical community, reflecting wide applications in biomedical research and healthcare field. However, prior efforts fail to make full use of the interaction between local and global contexts in biomedical document, and the derived performance needs to be improved accordingly. In this paper, we propose a novel framework for document-level CID relation extraction. More specifically, a stacked Hypergraph Aggregation Neural Network (HANN) layers are introduced to model the complicated interaction between local and global contexts, based on which better contextualized representations are obtained for CID relation extraction. In addition, the CID Relation Heterogeneous Graph is constructed to capture the information with different granularities and improve further the performance of CID relation classification. Experiments on a real-world dataset demonstrate the effectiveness of the proposed framework.

目的 探讨一次性多槽硅胶引流管应用于泌尿外科手术的临床效果.方法 选取行泌尿外科手术治疗且需术中放置肾周引流管的患者150例,将其随机分为观察组和对照组,每组75例.观察组患者放置一次性多槽硅胶引流管,对照组患者放置传统橡胶引流管.观察并比较2组患者术后24 h引流量、引流管留置时间、住院时间以及拔管时NRS疼痛程度.结果 与对照组相比,观察组患者术后24 h引流量更多,术后引流管留置时间、住院时间更短,术后拔管时轻度疼痛患者所占比例更高,差异均有统计学意义(P<0.05).结论 多槽硅胶引流管用于泌尿外科手术可以保证手术切口的有效引流,缓解术后疼痛,促进患者术后恢复.

提出了一种基于文档内位置关系的伪相关反馈框架LRoc(location-based Rocchio framework).该框架采用不同的核函数对候选词项在反馈文档中的位置进行建模,得到候选扩展词的位置重要度,并将其应用到经典的Rocchio模型中.该方法在选择和评估候选扩展词时,不仅考虑了词频,也考虑了词项位置的影响,有助于获取与查询更可能相关的扩展词.最后,在5种TREC数据集的实验结果表明:基于LRoc框架提出的3种模型(LRoc1、LRoc2和LRoc3)对比基线模型在MAP和P@20指标上具有显著提升.

目的 总结3例在机器人辅助胸腔镜下行小儿肺叶切除术的手术护理配合要点.方法 对先天性肺气道畸形或隔离肺行机器人辅助胸腔镜下肺叶切除术的患儿,通过特殊体位用物设计,规范小儿体位安置;根据人性化术前访视制定护理计划;细化术中配合预防术中并发症等实施护理.结果 3例机器人辅助胸腔镜下小儿肺叶切除术均顺利完成,无1例发生术中压力性损伤、低体温及其他术后并发症.结论 细致的手术观察和精细娴熟的手术配合,能保证机器人辅助胸腔镜下小儿肺叶切除术的手术安全.

The main goal of biomedical event extraction is to structurally extract biomedical events from texts, however, the specificity of the domain makes both text modeling and data annotation very difficult. We propose a self-supervised learning-based data augmentation method in this paper and design specific augmentation strategies for biomedical entities and event triggers in biomedical texts, which solves the problem of sparse annotation data to some extent. In addition we improve the reinforcement learning-based event extraction method to improve the training efficiency of the model. The experiments on two datasets demonstrate the effectiveness of our method.