OBJECTIVE: Home healthcare (HHC) clinical notes contain critical infection indicators that clinicians need in structured "indicator + context" pairs. Data sparsity and limited computing resources hinder automated extraction in decentralized HHC settings. This study developed and evaluated a resource-efficient pipeline using instruction-tuned, moderate-sized large language models (LLMs) to address these barriers. To address the data sparsity challenge, we also assessed the impact of a targeted LLM-based data augmentation strategy. MATERIALS AND METHODS: An expert-defined schema of 26 infection indicator categories was developed. We expanded the training set using a 3-stage workflow: targeted annotation, context mutation, and synthetic generation. We adapted 2 moderate-sized models (Gemma-12B and Qwen-14B) via Quantized Low-Rank Adaptation (QLoRA). We compared them to a larger-sized, prompted model and a smaller-sized, fully fine-tuned LLM. We evaluated all models on a held-out test set using partial micro-averaged F1 score, output reliability metrics, and qualitative error analysis. RESULTS: Instruction-tuned moderate-sized LLMs outperformed both baselines. The top-performing model, augmented Gemma-12B, achieved a partial micro-averaged F1 score of 0.879. LLM-based data augmentation enhanced overall performance, improving the identification of rare indicators and the interpretation of negations. The best model maintained a partial F1 score above 0.750 across all indicator categories. It also showed high format adherence, confirming its ability to generate reliable structured outputs. DISCUSSION: Instruction-tuning moderate-sized LLMs with QLoRA and targeted data augmentation enables high-accuracy extraction of infection indicators from HHC notes. CONCLUSION: This resource-efficient pipeline provides a scalable foundation for automated infection surveillance in healthcare settings with limited resources.

Existing cross-network node classification methods are mainly proposed for closed-set setting, where the source network and the target network share exactly the same label space. Such a setting is restricted in real-world applications, since the target network might contain additional classes that are not present in the source. In this work, we study a more realistic open-set cross-network node classification (O-CNNC) problem, where the target network contains all the known classes in the source and further contains several target-private classes unseen in the source. Borrowing the concept from open-set domain adaptation, all target-private classes are defined as an additional “unknown” class. To address the challenging O-CNNC problem, we propose an unknown-excluded adversarial graph domain alignment (UAGA) model with a separate-adapt training strategy. Firstly, UAGA roughly separates known classes from unknown class, by training a graph neural network encoder and a neighborhood-aggregation node classifier in an adversarial framework. Then, unknown-excluded adversarial domain alignment is customized to align only target nodes from known classes with the source, while pushing target nodes from unknown class far away from the source, by assigning positive and negative domain adaptation coefficient to known class nodes and unknown class nodes. Extensive experiments on real-world datasets demonstrate significant outperformance of the proposed UAGA over state-of-the-art methods on O-CNNC.

Recent progress in large language model (LLM)-based multi-agent collaboration highlights the power of structured communication in enabling collective intelligence.However, existing methods largely rely on static or graphbased inter-agent topologies, lacking the potential adaptability and flexibility in communication.In this work, we propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure, offering a significantly larger topology space for multi-agent communication.Our method focuses on two key directions: (1) Next-Agent Prediction, which selects the most suitable agent role at each step, and (2) Next-Context Selection (NCS), which enables each agent to selectively access relevant information from any previous step.Together, these components construct task-adaptive communication pipelines that support both role flexibility and global information flow.Extensive evaluations across multiple benchmarks demonstrate that our approach achieves superior performance while substantially reducing communication overhead.

OBJECTIVES: To optimize in-context learning in biomedical natural language processing by improving example selection. MATERIALS AND METHODS: We introduce a novel multi-mode retrieval-augmented generation (MMRAG) framework, which integrates 4 retrieval strategies: (1) Random Mode, selecting examples arbitrarily; (2) Top Mode, retrieving the most relevant examples based on similarity; (3) Diversity Mode, ensuring variation in selected examples; and (4) Class Mode, selecting category-representative examples. This study evaluates MMRAG on 3 core biomedical NLP tasks: Named Entity Recognition (NER), Relation Extraction (RE), and Text Classification (TC). The datasets used include BC2GM for gene and protein mention recognition (NER), DDI for drug-drug interaction extraction (RE), GIT for general biomedical information extraction (RE), and HealthAdvice for health-related text classification (TC). The framework is tested with 2 large language models (Llama-2-7B and Llama-3-8B) and 3 retrievers (Contriever, MedCPT, and BGE-Large) to assess performance across different retrieval strategies. RESULTS: The results from the Random Mode indicate that providing more examples in the prompt improves the model's generation performance. Meanwhile, Top Mode and Diversity Mode significantly outperform Random Mode on the RE (DDI) task, achieving an F1 score of 0.9669-a 26.4% improvement. Among the 3 retrievers tested, Contriever outperformed the other 2 in a greater number of experiments. Additionally, Llama 2 and Llama 3 demonstrated varying capabilities across different tasks, with Llama 3 showing a clear advantage in handling NER tasks. CONCLUSION: MMRAG effectively enhances biomedical in-context learning by refining example selection, mitigating data scarcity issues, and demonstrating superior adaptability for NLP-driven healthcare applications.

OBJECTIVE: To develop an advanced multi-task large language model (LLM) framework for extracting diverse types of information about dietary supplements (DSs) from clinical records. METHODS: We focused on 4 core DS information extraction tasks: named entity recognition (2 949 clinical sentences), relation extraction (4 892 sentences), triple extraction (2 949 sentences), and usage classification (2 460 sentences). To address these tasks, we introduced the retrieval-augmented multi-task information extraction (RAMIE) framework, which incorporates: (1) instruction fine-tuning with task-specific prompts; (2) multi-task training of LLMs to enhance storage efficiency and reduce training costs; and (3) retrieval-augmented generation, which retrieves similar examples from the training set to improve task performance. We compared the performance of RAMIE to LLMs with instruction fine-tuning alone and conducted an ablation study to evaluate the individual contributions of multi-task learning and retrieval-augmented generation to overall performance improvements. RESULTS: Using the RAMIE framework, Llama2-13B achieved an F1 score of 87.39 on the named entity recognition task, reflecting a 3.51% improvement. It also excelled in the relation extraction task with an F1 score of 93.74, a 1.15% improvement. For the triple extraction task, Llama2-7B achieved an F1 score of 79.45, representing a significant 14.26% improvement. MedAlpaca-7B delivered the highest F1 score of 93.45 on the usage classification task, with a 0.94% improvement. The ablation study highlighted that while multi-task learning improved efficiency with a minor trade-off in performance, the inclusion of retrieval-augmented generation significantly enhanced overall accuracy across tasks. CONCLUSION: The RAMIE framework demonstrates substantial improvements in multi-task information extraction for DS-related data from clinical records.

Generating recommendation reasons for recommendation results is a long-standing problem because it is challenging to explain the underlying reasons for recommending an item based on user and item IDs.Existing models usually learn semantic embeddings for each user and item, and generate the reasons according to the embeddings of the user-item pair.However, user and item IDs do not carry inherent semantic meaning, thus the limited number of reviews cannot model users' preferences and item characteristics effectively, negatively affecting the model generalization for unseen user-item pairs.To tackle the problem, we propose the Concept Enhanced Explainable Recommendation framework (CEER), which utilizes macro concepts as the intermediary to bridge the gap between the user/item embeddings and the recommendation reasons.Specifically, we maximize the information bottleneck to extract macro concepts from user-item reviews.Then, for recommended user-item pairs, we jointly train the concept embeddings with the user and item embeddings, and generate the explanation according to the concepts.Extensive experiments on three datasets verify the superiority of our CEER model.

Graph anomaly detection (GAD) has wide applications in real-world networked systems. In many scenarios, people need to identify anomalies on new (sub)graphs, but they may lack labels to train an effective detection model. Since recent semi-supervised GAD methods, which can leverage the available labels as prior knowledge, have achieved superior performance than unsupervised methods, one natural idea is to directly adopt a trained semi-supervised GAD model to the new (sub)graphs for testing. However, we find that existing semi-supervised GAD methods suffer from poor generalization issues, i.e., well-trained models could not perform well on an unseen area (i.e., not accessible in training) of the graph. Motivated by this, we formally define the problem of generalized graph anomaly detection that aims to effectively identify anomalies on both the training-domain graph(s) and the unseen test graph(s). Nevertheless, it is a challenging task since only limited labels are available, and the normal data distribution may differ between training and testing data. Accordingly, we propose a data augmentation method namedAugAN(Augmentation forAnomaly andNormal distributions) to enrich training data and adopt a customized episodic training strategy for learning with the augmented data. Extensive experiments verify the effectiveness ofAugANin improving model generalizability.

Network anomaly detection is a crucial task since a few anomalies can cause huge losses. Semi-supervised anomaly detection methods can effectively leverage a small number of labels as prior knowledge to enhance detection accuracy. But in real-world scenarios, novel types of anomalies (i.e., unseen anomalies) usually exist on networks which may present different characteristics with the seen anomalies and are hard to be identified by prior semi-supervised anomaly detection methods. In this paper, we propose the novel problem of unseen network anomaly detection that aims to identify both seen and unseen anomalies to eliminate potential dangers. Accordingly, we propose a method called Multi-hypersphere Graph Learning (MHGL) to effectively leverage existing labels by learning fine-grained normal patterns to discriminate anomalies. Experiments demonstrate that MHGL outperforms state-of-the-art methods significantly.

Attributed network anomaly detection is essential in various networked systems. It aims to detect nodes that significantly deviate from their corresponding background. In conventional anomaly detection, the background is defined as the vast majority. But in networks, anomalies can be local and look normal when compared with the majority. While several efforts have explored to consider communities as the background, it remains challenging to learn suitable communities for effective anomaly detection. Also, the patterns of anomalies are unknown and it is nontrivial to define criteria of anomalies. To bridge the gap, in this paper, we argue that, by using appropriate models, it is sufficient to simply consider neighbor nodes as the background to detect anomalies. Correspondingly, we propose a novel abnormality-aware graph neural network (AAGNN). It utilizes subtractive aggregation to represent each node as the deviation from its neighbors (the background). Normal nodes with high confidence are employed as labels to learn a tailored hypersphere as the criterion of anomalies. Experiments demonstrate that AAGNN surpasses state-of-the-art methods significantly.