Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning [0.03%]
利用机器学习从大规模电子健康记录中识别阿尔茨海默病的进展亚表型
Manqi Zhou,Alice S Tang,Hao Zhang et al.
Manqi Zhou et al.
Objective: Identification of clinically meaningful subphenotypes of disease progression can enhance the understanding of disease heterogeneity and underlying pathophysiology. In this study, we propose a machine learning f...
Enhancing data quality in medical concept normalization through large language models [0.03%]
通过大型语言模型改进医学概念规范化中的数据质量
Haihua Chen,Ruochi Li,Ana Cleveland et al.
Haihua Chen et al.
Objective: Medical concept normalization (MCN) aims to map informal medical terms to formal medical concepts, a critical task in building machine learning systems for medical applications. However, most existing studies o...
A practical guide to usability questionnaires that evaluate clinicians' perceptions of health information technology [0.03%]
评估临床医生对卫生信息科技感知的实用版效用调查指南
Alissa L Russ-Jara,Jason J Saleem,Jennifer Herout
Alissa L Russ-Jara
Objective: Numerous usability questionnaires are available to evaluate the usability of health information technology (IT). It can be difficult for practitioners to determine which questionnaire most closely aligns with t...
Scalable and efficient on-chain data management in blockchain for large biomedical data [0.03%]
区块链中用于大规模生物医学数据的可扩展和高效链上数据管理
Eric Ni,Elizabeth Knight,Mark Gerstein
Eric Ni
Blockchain technology is gaining traction in the biomedical sector due to its ability to improve trust and reduce the risk of fraud and errors in health data management. However, the large volume of biomedical datasets has slowed its adopti...
A systematic mapping study of semantic technologies in multi-omics data integration [0.03%]
多组学数据集成中语义技术的系统映射研究
Giovanni Maria De Filippis,Domenico Amalfitano,Cristiano Russo et al.
Giovanni Maria De Filippis et al.
Objective: The integration of multi-omics data is essential for understanding complex biological systems, providing insights beyond single-omics approaches. However, challenges related to data heterogeneity, standardizati...
PLAGCA: Predicting protein-ligand binding affinity with the graph cross-attention mechanism [0.03%]
PLAGCA:利用图交叉注意力机制预测蛋白质配体结合亲和力
Ming-Hui Shi,Shao-Wu Zhang,Qing-Qing Zhang et al.
Ming-Hui Shi et al.
Accurate prediction of protein-ligand binding affinity plays a crucial role in drug discovery. However, determining the binding affinity of protein-ligands through biological experimental approaches is both time-consuming and expensive. Alt...
Integrating Mendelian randomization and literature-mined evidence for breast cancer risk factors [0.03%]
整合孟德尔随机化和文献证据来分析乳腺癌风险因素
Marina Vabistsevits,Tim Robinson,Ben Elsworth et al.
Marina Vabistsevits et al.
Objective: An increasing challenge in population health research is efficiently utilising the wealth of data available from multiple sources to investigate disease mechanisms and identify potential intervention targets. T...
A graph neural network explainability strategy driven by key subgraph connectivity [0.03%]
由关键子图连接驱动的图神经网络可解释性策略
L N Dai,D H Xu,Y F Gao
L N Dai
Current explainability strategies for Graph Neural Networks (GNNs) often focus on individual nodes or edges, neglecting the significance of key subgraphs in decision-making processes. This limitation can result in dispersed and less reliabl...
Ontology-driven identification of inconsistencies in clinical data: A case study in lung cancer phenotyping [0.03%]
本体驱动的临床数据不一致性识别：肺癌表型化中的一个案例研究
Yvon K Awuklu,Fleur Mougin,Romain Griffier et al.
Yvon K Awuklu et al.
Objective: To illustrate the use of an ontology in evaluating data quality in the medical field, focusing on phenotyping lung cancers. Materials and metho...
Uncovering hidden subtypes in dementia: An unsupervised machine learning approach to dementia diagnosis and personalization of care [0.03%]
揭开痴呆症中的隐藏亚型：一种无监督机器学习的痴呆诊断和个性化护理方法
Andrea Campagner,Luca Marconi,Edoardo Bianchi et al.
Andrea Campagner et al.
Objective: Dementia represents a growing public health challenge, affecting an increasing number of individuals. It encompasses a broad spectrum of cognitive impairments, ranging from mild to severe stages, each of which ...