Knowledge-Guided and Reinforced Selective State Space Model for radiology report generation [0.03%]
知识引导和强化的选择性状态空间模型在放射学报告生成中的应用
Ziyang Li,Dedong Yang,Rongtao Li et al.
Ziyang Li et al.
Objective: To address the limitations of "black box" sequence models and simplistic reward functions in radiology report generation, we aim to improve clinical accuracy and linguistic quality by integrating explicit medic...
Model utility and explainability in federated learning - A case study in healthcare using fundus oculi datasets [0.03%]
联邦学习中的模型实用性和可解释性——一种使用眼底数据的医疗案例研究
Niklas Penzel,Daniel Scheliga,Hannes Oppermann et al.
Niklas Penzel et al.
Objective: Introduce a case study for Federated Learning (FL) in healthcare, addressing challenges posed by patient privacy and limited large-scale datasets. Our goal is to assess the features learned by FL methods in a s...
Enhancing adverse drug event extraction and summarization for cancer drugs through large language models [0.03%]
通过大规模语言模型增强癌症药物的不良事件提取和总结能力
Sofia Jamil,Sriparna Saha,Rajiv Misra
Sofia Jamil
Purpose: Cancer treatments such as chemotherapy, targeted therapy, and immunotherapy can effectively combat malignant cells but frequently cause serious side effects by damaging healthy tissues. This underscores the need ...
Scalable discovery and validation of order-specific electronic health record event trajectories for interpretable adverse-outcome risk estimation [0.03%]
可扩展地发现和验证订单特定的电子健康记录事件轨迹以进行可解释的不良结果风险估计
Brice Edelman,Hannah Kim,Jeffrey Skolnick
Brice Edelman
Objective: Clinicians must estimate patients' risk of adverse outcomes, yet many electronic health record tools represent health history as an unordered "bag of codes," discarding temporal order. Machine learning tools th...
Context-Aware adaptive normalization LSTM (CAAN-LSTM) for immunotherapy decision support in cancer clinical data analysis [0.03%]
基于上下文自适应规范的LSTM（CAAN-LSTM）在癌症临床数据免疫治疗决策支持中的应用
Rian Balafkhar,Yaser Baalawi,Abdullah Mohammed Almashhor et al.
Rian Balafkhar et al.
Background: Clinical decision-making for cancer immunotherapy is challenged by the heterogeneous and often incomplete nature of patient time-series data. Traditional models struggle to account for individual patient varia...
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 ...