A federated learning framework for ethical dynamic treatment allocation across heterogeneous hospitals [0.03%]
一种伦理的动态治疗分配的联邦学习框架用于异构医院
Xenia Konti,Nicoleta J Economou-Zavlanos,Yi Shen et al.
Xenia Konti et al.
Objective: In this paper, we propose an adaptive federated learning framework to learn optimal treatments for individual hospitals that possibly serve different patient populations. The proposed framework can enable the d...
DMDGRN: A data augmentation-based multilayer directed graph convolutional network for gene regulatory network inference [0.03%]
基于数据增强的多层有向图卷积网络的基因调控网络推理方法(DMDGRN)
Pi-Jing Wei,Mingzhu Sun,Zheng Ding et al.
Pi-Jing Wei et al.
Objective: Gene regulatory networks (GRNs) provide a graphical representation of the regulatory interactions between transcription factors (TFs) and their target genes, governing transcriptional states that define cell id...
FG-DDI: Functional group-aware graph neural networks for drug-drug interaction prediction [0.03%]
基于功能基团感知的预测药物相互作用的图神经网络
Fangyu Zhou,Shahadat Uddin
Fangyu Zhou
Objective: We aim to improve Drug-Drug Interactions (DDIs) by explicitly injecting medicinal-chemistry knowledge of functional groups (FGs) into graph neural network (GNN) message passing, in both transductive and inducti...
Quantifying the effect of Behaviour Self-regulation on well-being through causal analysis: A methodological framework for longitudinal health data [0.03%]
基于因果推断的行为自我调节对健康的纵向影响量化研究：一种框架性的方法学探索
Jialou Wang,Pingfan Wang,Wai Lok Woo et al.
Jialou Wang et al.
Understanding the drivers of well-being from longitudinal behavioral data is a fundamental challenge in biomedical informatics, where traditional analyses often conflate correlation with causation. This paper presents a rigorous application...
Beyond Fine-Tuning: Leveraging Domain-Aware In-Context learning with large language models for clinical named entity recognition [0.03%]
超越微调：利用领域感知的上下文学习方法，使用大规模语言模型进行临床实体识别
Siun Kim,David Seung U Lee,Yujin Kim et al.
Siun Kim et al.
Background: Clinical named entity recognition (NER) is essential for structuring clinical narratives. While large language model (LLM)-based in-context learning (ICL) enables parameter-free adaptation, encoder-based fine-...
RINet: synthetic data training for indirect estimation of clinical reference distributions [0.03%]
基于合成数据训练的临床参考分布间接估计方法
Jack LeBien,Julian Velev,Abiel Roche-Lima
Jack LeBien
Background: Indirect methods for estimating clinical reference intervals (RIs) use statistical analysis to identify non-pathological sub-distributions within large datasets acquired from routine clinical testing. This app...
A computational framework for predicting drug-target interactions by fusing gene ontology information with cross attention [0.03%]
一种通过融合基因本体信息与跨注意力预测药物-靶点相互作用的计算框架
Wenchao Cui,Pingjian Ding,Lingyun Luo et al.
Wenchao Cui et al.
Motivation: Identifying drug-target interactions (DTIs) is a critical step in both drug discovery and drug repurposing. Accurate in silico prediction of DTIs can substantially reduce development time and costs. Recent adv...
Integrating retrospective quality assessment with real-time guideline application to support the episodic application of clinical guidelines over significant time periods [0.03%]
结合回顾性质量评估与实时指南应用以支持临床指南在较长时期内的阶段性应用
Bruria Ben Shahar,Yuval Shahar,Shai Jaffe et al.
Bruria Ben Shahar et al.
Background: Evidence-based clinical guidelines (GLs) are essential for standardizing care, yet often difficult to apply. Most clinical decision support systems (CDSSs) assume continuous application, which misaligns with t...
Augmented intelligence for multimodal virtual biopsy in breast cancer using generative artificial intelligence [0.03%]
基于生成式人工智能的乳腺癌多模态虚拟活检增强智能技术
Aurora Rofena,Claudia Lucia Piccolo,Bruno Beomonte Zobel et al.
Aurora Rofena et al.
Objective: This study aims to propose a multimodal, multi-view deep learning approach for breast cancer virtual biopsy, a non-invasive classification of breast lesions as malignant or benign, by integrating Full-Field Dig...
Fusion framework: Conditional-aware one-stage nested event extraction model [0.03%]
融合框架条件感知一站式嵌套事件抽取模型
Sen Niu,Xiaohong Han,Liu Cao et al.
Sen Niu et al.
We present CA-NEE, a Conditional-Aware one-stage model for overlapping and nested biomedical event extraction. CA-NEE integrates an event-type-aware conditioning mechanism with token-pair relation modeling to jointly identify triggers, argu...