Quality-aware multi-source data fusion and enhancement for Medical Concept Normalization using Large Language Models [0.03%]
基于大规模语言模型的质量感知多源数据融合与增强的医学概念规范化方法
Yuhan Zhou,Ruochi Li,Ana Cleveland et al.
Yuhan Zhou et al.
Objective: Medical Concept Normalization (MCN) maps informal health phrases to formal clinical concepts. It is a critical task for pharmacovigilance, patient record analysis, and health-related text mining. Extensive MCN ...
Corrigendum to "Selection-based prompting with synthesized candidates unlocks high-performance clinical abbreviation disambiguation via open-source LLMs" [J. Biomed. Inform. 181 (2026) 105085] [0.03%]
“基于选择的提示与合成候选解锁高绩效临床缩写消歧化研究”(基于开源LLM)[期刊文章编号181(2026)105085]的勘误表
Siwen Wei,Xiaoyu Liu,Yonghong Shi
Siwen Wei
Published Erratum
Journal of biomedical informatics. 2026 Oct:182:105091. DOI:10.1016/j.jbi.2026.105091 2026
CGX: OCR-enhanced knowledge graph retrieval for explainable heart failure analysis [0.03%]
基于OCR增强知识图谱检索的心力衰竭可解释分析方法
Dat T Nguyen,Anh N Le,Binh T Trinh et al.
Dat T Nguyen et al.
Knowledge graphs are increasingly used to organize and retrieve complex medical information, yet existing graph-based retrieval systems often suffer from high construction costs, limited scalability as knowledge grows, and limited interpret...
A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations [0.03%]
一种新颖的语言模型可从临床试验的前瞻性注册中预测严重不良事件结果
Qixuan Hu,Xumou Zhang,Jinman Kim et al.
Qixuan Hu et al.
Objectives: With accurate estimates of expected safety results, clinical trials could be better designed and monitored. We evaluated methods for predicting serious adverse event (SAE) results in clinical trials using info...
Predicting time-to-event clinical outcomes with multivariate repeated measurements of patient-level covariates: A systematic review [0.03%]
基于患者层面协变量的多变量重复测量预测时间至事件临床结局：系统综述
Irene Cara,Frederik H Erdmann,Chiara Attanasio et al.
Irene Cara et al.
Objective: Repeated measurements capture the progression of health over time and may inform survival prediction. The goal of this review is to identify and compare how the presented methodologies for survival prediction w...
Selection-based prompting with synthesized candidates unlocks high-performance clinical abbreviation disambiguation via open-source LLMs [0.03%]
基于合成候选项的选择性提示通过开源LLM实现高性能临床缩写消解
Siwen Wei,Xiaoyu Liu,Yonghong Shi
Siwen Wei
Objective: Clinical abbreviations introduce semantic ambiguity that hinders automated understanding in healthcare informatics. While generative large language models (LLMs) show promise, direct generation often lacks clin...
Performance of large language models in data extraction for evidence synthesis: A systematic review [0.03%]
大型语言模型在证据综合数据提取中的表现：系统评价
Ravi Shankar,Amaevia Lim,Xu Qian
Ravi Shankar
Objective: Data extraction is among the most resource-intensive and error-prone stages of systematic review production. Large language models (LLMs) offer potential for automating or semi-automating this process, yet thei...
Selective classification under imbalance in multiclass settings: A novel metric for bias-aware risk-coverage evaluation [0.03%]
不平衡多类设置下的选择性分类：一种偏见感知风险覆盖率评估的新指标
Fatih Sağlam,Ünsal Özgen,Alper Uygun et al.
Fatih Sağlam et al.
Objective: Selective classification improves reliability by allowing models to abstain on uncertain inputs, which is critical in safety-sensitive domains such as healthcare. However, commonly used evaluation metrics obscu...
Predicting healthy weight status from physical activity and dietary intake: A time-aware data mining pipeline [0.03%]
基于时间感知的数据挖掘管道预测身体活动和膳食摄入的健康体重状态
Xiaotong Yu,Joshua Y Kim,Guillaume Wattelez et al.
Xiaotong Yu et al.
Objective: To develop and evaluate a time-aware data mining pipeline that integrates accelerometry-based Physical Activity (PA), static dietary intake, and sociodemographic factors to predict Healthy Weight Status (HWS), ...
BiGranMolNet: A deep learning method for predicting blood-brain barrier permeability based on Bi-Granularity Molecular Graphs [0.03%]
基于双粒度分子图的深度学习方法预测血脑屏障通透性
Yidan Wang,Yizhuo Wang,Yang Wang et al.
Yidan Wang et al.
Objective: The selective permeability of the blood-brain barrier (BBB) hinders the delivery of central nervous system (CNS) drugs to brain targets. It is therefore crucial to determine BBB permeability during CNS drug dev...