CTKGR: temporal knowledge graph enhanced prediction of in-hospital events for pneumonia patients using electronic health records [0.03%]
基于电子健康记录的肺炎患者住院期间事件预测的时序知识图谱方法
Yingliang Yang,Binyu Gao,Chunting Tan et al.
Yingliang Yang et al.
Objective: This study aims to develop an advanced clinical event prediction model leveraging the temporal characteristics embedded within electronic health record (EHR), with a specific focus on predicting clinical events...
A comprehensive survey on medical concept normalization: Datasets, techniques, applications, and future directions [0.03%]
医疗概念规范化的全面调查：数据集、技术、应用和未来方向
Haihua Chen,Yuhan Zhou,Ruochi Li et al.
Haihua Chen et al.
Medical concept normalization (MCN) involves mapping informal medical terms or phrases to standardized medical concepts, serving a crucial role in medical text analysis, healthcare intelligence, and other applications. Previous studies have...
Bidirectional data harmonization across the multiple chronic disease disparities research consortium: A pipeline description of opportunities and challenges [0.03%]
跨多慢性疾病差异研究联盟的双向数据协调：机遇与挑战的机会和挑战.pipeline描述
Hyelee Kim,Shuang Liang,Kathy Lanier et al.
Hyelee Kim et al.
Objective: The Multiple Chronic Disease Disparities Research (MCD-DR) Consortium investigates ways to prevent and manage multiple chronic conditions in diverse populations. Data harmonization (DH) integrates and pools div...
Integrating histology and spatial transcriptomics via multimodal transformers and contrastive representation learning for accurate gene expression prediction [0.03%]
基于多模态变压器和对比表示学习的整合组织学和空间转录组学的准确基因表达预测方法
Kai Wang,Liuming Shi,Xue Li et al.
Kai Wang et al.
Predicting spatial gene expression from Histological images is a fundamental task in understanding tissue organization and molecular phenotypes. However, existing methods often rely on single-model representations or lack effective alignmen...
UCVA ontology: Standardizing local context factors to support the analysis of unwarranted clinical variation [0.03%]
UCVA本体：通过标准化地方环境因素来支持分析临床不合理变异
Apollo McOwiti,Xubing Hao,Rebecca Z Lin et al.
Apollo McOwiti et al.
Objectives: Unwarranted clinical variation (UCV), defined as care provided to a patient that is not proportional to the patient's needs, clinical characteristics, or preferences, results in negative patient outcomes. UCV ...
Explainable multimodal deep learning models for variable-length sequences in critically ill patients [0.03%]
重症患者变长序列的可解释多模态深度学习模型
Jennifer Martin,Majid Afshar,Askar Safipour Afshar et al.
Jennifer Martin et al.
Objective: Deep learning models have shown strong performance in predicting clinical events in critical care using structured electronic health record (EHR) data. While incorporating unstructured notes improves accuracy, ...
David da Costa Correia,Francisco M Couto,Hugo Martiniano
David da Costa Correia
Objective: Non-coding RNAs (ncRNAs) are involved in various important biological processes, relevant disease biomarkers and therapeutic agents. However, information remains sparsely distributed, mostly in articles. It is ...
Multi-level dynamic fusion and temporal role-aware network for diagnosis prediction [0.03%]
多层次动态融合与时间角色感知网络在诊断预测中的应用
Zhuang Fu,Youshen Chi,Xiaojing Yu et al.
Zhuang Fu et al.
Disease association modeling and dynamic evolution analysis in electronic health records (EHR) is a core challenge in diagnosis prediction. Existing approaches typically treat diagnostic codes as independent entities, ignore the clinical si...
Harrison Fah,Russell Greiner,Roger A Dixon
Harrison Fah
Objective: Traditional survival prediction models use a patient's covariates at a single time point to estimate the time until a specific event occurs, such as death or hospital readmission. However, in many longitudinal ...
AFTS: A patient-agnostic encoder-decoder architecture with directional attention for blood glucose forecasting [0.03%]
基于定向注意力的编码解码架构在血糖预测中的应用
Yu Chen,Henghong Lin,Zhijin Wang et al.
Yu Chen et al.
Accurate blood glucose forecasting remains challenging due to inter-patient heterogeneity and complex glycemic dynamics. We present AFTS (Adaptive Feature Time Series), a patient-agnostic deep learning architecture combining a bidirectional...