A Machine Learning Approach to Voice-Based Parkinson Disease Screening Using Multiview Spectrogram and Speech Recognition Features: Diagnostic Study [0.03%]
基于机器学习的帕金森病语音筛查方法：诊断研究
Arifa Zahir,Jaehong Yu,Jin-Sun Jun et al.
Arifa Zahir et al.
Background: Parkinson disease frequently manifests early vocal impairment, motivating the development of noninvasive and scalable digital screening tools. ...
Machine Learning for Intraoperative Bleeding Prediction in Patients Undergoing Surgery: Scoping Review [0.03%]
手术患者术中出血预测的机器学习研究：系统综述
Shiqiong Yan,Ping Zhang,Wanwan Qiao et al.
Shiqiong Yan et al.
Background: Intraoperative bleeding is a critical event that impacts surgical safety and patient outcomes. Machine learning (ML) has demonstrated potential in prediction tasks, yet its methodological rigor and clinical tr...
Understanding Transformer-Based Classifications of Medical Text Using a Large Language Model for the Attribution of Feature Importance: Proof-of-Concept Algorithm Development and Validation Study [0.03%]
利用大型语言模型进行特征重要性归属以理解基于变压器的医学文本分类：概念验证算法开发与验证研究
Fangwen Zhou,Ashirbani Saha,Muhammad Afzal et al.
Fangwen Zhou et al.
Background: Deep learning, particularly encoder-only transformer architectures, has demonstrated excellent performance in biomedical literature classification, facilitating evidence-based medicine, and knowledge synthesis...
Predicting 5-Year Mortality in Non-Small-Cell Lung Cancer Using the Korean Central Cancer Registry: Model Development and Validation Study [0.03%]
基于韩国全国癌症登记数据库的非小细胞肺癌患者五年生存预测模型的研制与验证研究
Jong Hyuk Lee,Ho Cheol Kim,Kyu-Won Jung et al.
Jong Hyuk Lee et al.
Background: Non-small-cell lung cancer (NSCLC) is one of the most common cancers and a leading cause of cancer-related mortality, making prognostic prediction clinically essential. Machine learning models are increasingly...
Knowledge, Attitudes, Practices, Barriers, and Promotional Strategies Related to Clinical Data Interchange Standards Consortium Adoption Among Clinical Data Management Professionals: Semiqualitative Interview Study [0.03%]
临床数据管理人员之间关于临床数据交换标准协会采用的相关知识、态度、实践、障碍和推广策略：半定性访谈研究
Gaoqiang Xie,Jing He,Wenyao Ma et al.
Gaoqiang Xie et al.
Background: National Medical Products Administration of China has actively encouraged organizations to adopt the Clinical Data Interchange Standards Consortium (CDISC) for clinical data submission since 2020. ...
Predicting End-Stage Renal Disease and Mortality in Chronic Kidney Disease Using Machine Learning: Retrospective Cohort Study [0.03%]
基于机器学习预测慢性肾脏病患者终末期肾病和死亡的回顾性队列研究
Tz-Heng Chen,Kuan-Hsun Lin,Yang Ho et al.
Tz-Heng Chen et al.
Background: Chronic kidney disease (CKD) is a global health burden characterized by heterogeneous progression trajectories. Without timely and appropriate management, CKD can lead to increased morbidity and mortality and ...
Histopathological Assessment of Myocardial Ischemia-Reperfusion Injury Using Transformer-Based Artificial Intelligence: Model Comparison Study [0.03%]
基于变压器的人工智能评估心肌缺血再灌注损伤的病理组织学的模型比较研究
Chengnan Liu,Min Xu,Yanxia Lv et al.
Chengnan Liu et al.
Background: Myocardial ischemia-reperfusion injury (MIRI) poses diagnostic challenges due to complex histopathological changes. Objective: ...
Early Immunological Biomarkers for Personalized Treatment Selection in Severe COVID-19: Post Hoc Machine Learning Analysis of a Randomized Clinical Trial [0.03%]
重症COVID-19个性化治疗选择的早期免疫生物标志物：随机临床试验的事后机器学习分析
Symeon Savvopoulos,Anastasia Papadopoulou,Georgios Karavalakis et al.
Symeon Savvopoulos et al.
Background: Severe COVID-19 is a global health concern despite continuous vaccination campaigns because current therapies, such as dexamethasone and remdesivir, do not considerably improve immune function, especially in h...
Improving Radiology Report Error Detection Using a Multipass Large Language Model: Framework Development and Validation [0.03%]
使用多遍大型语言模型改进放射学报告错误检测：框架开发与验证
Songsoo Kim,Seungtae Lee,See Young Lee et al.
Songsoo Kim et al.
Background: Large language model (LLM) proofreaders for radiology reports generate many false positives (FPs) due to the low prevalence of errors. Objecti...
Advancing Gastrointestinal Cancer Risk Prediction With Patient-Centered Machine Learning: Machine Learning Modeling Study [0.03%]
基于患者中心的机器学习推进胃肠癌风险预测：机器学习建模研究
Daina Baublyte,Jeonghee Lee,Madhawa Gunathilake et al.
Daina Baublyte et al.
Background: Gastrointestinal (GI) cancers are a significant health concern in South Korea. Recently, machine learning (ML) models have emerged as powerful tools to support early screening efforts and identify people at ri...