Multi-domain ECG feature extraction enable accurate automated recognition of bipolar disorder, depression and schizophrenia [0.03%]
跨域心电图特征提取可实现对双相情感障碍、抑郁和精神分裂症的精准自动识别
Guangmeng Xue,Bin Chen,Yuanyuan Zhang et al.
Guangmeng Xue et al.
Objective: To develop and validate a multi-lead electrocardiogram (ECG)-based machine learning system for automated classification of major psychiatric disorders (bipolar disorder, major depressive disorder, and schizophr...
Irina Sousa Moreira,Ricardo Cardoso Pereira,Pedro Pereira Rodrigues et al.
Irina Sousa Moreira et al.
Heat and hearts: An exposure-anchored computational phenotyping framework for assessing cardiovascular vulnerability during extreme heat [0.03%]
热与心：一种暴露为基础的计算表型框架，用于评估极端高温下的心血管脆弱性
Peter M Graffy,Benjamin W Barrett,Daniel E Horton et al.
Peter M Graffy et al.
Objective: Extreme heat is associated with increased cardiovascular vulnerability. We developed and validated a heat exposure-anchored cardiovascular vulnerability (HECV) computational phenotype for outpatient visits usin...
Challenges of health data standard adoption and usage: a systematic review [0.03%]
卫生数据标准采用与应用的挑战：系统综述
Alberto Marfoglia,Valerio Antonio Arcobelli,Serena Moscato et al.
Alberto Marfoglia et al.
Objective: To explore the adoption and practical implementation of the three major health data standards (i.e., FHIR, OMOP-CDM, and openEHR), to evaluate their maturity level in terms of how extensively they have been app...
PlainQAFact: Retrieval-augmented factual consistency evaluation metric for biomedical plain language summarization [0.03%]
基于检索的生物医学文本生成事实一致性评价指标 PlainQAFact
Zhiwen You,Yue Guo
Zhiwen You
Hallucinated outputs from large language models (LLMs) pose risks in the medical domain, especially for lay audiences making health-related decisions. Existing automatic factual consistency evaluation methods, such as entailment- and questi...
Retrieval-augmented in-context learning for multimodal large language models in disease classification [0.03%]
基于检索的增强上下文学习在多模态大型语言模型疾病分类中的应用
Zaifu Zhan,Shuang Zhou,Xiaoshan Zhou et al.
Zaifu Zhan et al.
Objective: We aim to dynamically retrieve informative demonstrations, enhancing in-context learning in multimodal large language models (MLLMs) for disease classification. ...
Personalized federated learning for medical vision-language models via efficient fine-tuning and uncertainty-aware disentanglement [0.03%]
基于高效微调和不确定性感知解耦的医学视觉语言模型个性化联邦学习
He Zhu,Ren Togo,Takahiro Ogawa et al.
He Zhu et al.
Privacy concerns and data heterogeneity remain critical barriers to advancing medical AI. While federated learning (FL) offers a decentralized solution, it often suffers from the Subspace Mismatch problem in clinical settings, where non-ind...
Exploring efficiency frontiers of thinking budget in medical reasoning: Scaling laws between computational resources and reasoning quality [0.03%]
思考预算在医学推理中的效率前沿：计算资源与推理质量之间的标度定律
Ziqian Bi,Lu Chen,Hongying Luo et al.
Ziqian Bi et al.
Objective: To establish a generalizable methodological framework for thinking budget optimization in medical reasoning, deriving predictive scaling laws and operationalizable efficiency regimes that enable principled comp...
Linear and non-linear feature selection for survival modelling of ischemic heart disease in patients with diabetes: Insights from electronic health records [0.03%]
基于电子健康记录的糖尿病患者缺血性心脏病生存模型的线性和非线性特征选择：洞察
Róbert Bata,Amr Sayed Ghanem,Attila Csaba Nagy
Róbert Bata
Cardiovascular disease, particularly ischemic heart disease (IHD), is a major cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). Accurate risk prediction is essential, yet the influence of linear versus non-l...
Composite pathway-guided Cox modeling for interpretable survival prediction in breast cancer [0.03%]
基于复合途径的Cox模型在乳腺癌生存预测中的可解释性研究
Mohamed S Hefny
Mohamed S Hefny
Accurate survival prediction in breast cancer is essential for patient risk stratification and personalized treatment planning. Although transcriptomic data offer valuable insights into tumor biology, existing predictive models often suffer...