I-ETL: an interoperability-aware health (meta)data pipeline to enable federated analyses [0.03%]
一种新的健康（元）数据转换管道I-ETL，支持联合分析跨机构的生物医学大数据
Nelly Barret,Anna Bernasconi,Boris Bikbov et al.
Nelly Barret et al.
Background: Clinicians are interested in better understanding complex diseases, such as cancer or rare diseases, so they need to produce and exchange data to mutualize sources and join forces. To do so and ensure privacy,...
GPT-4o and the quest for machine learning interpretability in ICU risk of death prediction [0.03%]
探索ICU死亡风险预测中机器学习的可解释性——迈向GPT-4o
Moein E Samadi,Kateryna Nikulina,Sebastian Johannes Fritsch et al.
Moein E Samadi et al.
Background: Clinical utilization of machine learning is hampered by the lack of interpretability inherent in most non-linear black box modeling approaches, reducing trust among clinicians and regulators. Advanced large la...
Reinforcement learning for proposing smoking cessation activities that build competencies: Combining two worldviews in a virtual coach [0.03%]
增强学习在提出戒烟活动中的应用：虚拟教练中两种世界观的结合以培养能力
Nele Albers,Mark A Neerincx,Willem-Paul Brinkman
Nele Albers
Background: Reaching personal goals typically requires building competencies (e.g., insights into personal strengths), but expert health professionals and non-expert clients often think differently about which competencie...
Factors influencing the availability and use of electronic medical records systems in public health facilities in Uganda: a cross-sectional assessment [0.03%]
影响乌干达公共医疗机构电子病历系统可获得性和使用的因素：横断面评估
Anthony Ddamba,Benard Nsubuga,Moses Kamabare et al.
Anthony Ddamba et al.
Background: The advancement of information and communication technology (ICT) has significantly accelerated the adoption and utilisation of Electronic Medical Record (EMR) systems in both developed and developing countrie...
Barriers and facilitators to shared decision-making for patients with cancer and health care providers based on the COM-B model: a systematic review [0.03%]
基于COM-B模型的癌症患者和卫生保健提供者共决面临的障碍与促进因素系统评价
Lisi Duan,Ting Wang,Yinning Guo et al.
Lisi Duan et al.
Background: With advancements in cancer treatment approaches, patients face increasingly complex decisions regarding their care and treatment. Although Shared Decision-Making (SDM) can help patients make more informed and...
Predicting outcomes in pediatric patients with acute kidney injury: a retrospective single-center cohort study using machine learning models [0.03%]
基于机器学习模型的儿科急性肾损伤患者预后评估：单中心回顾性队列研究
Feifei Shen,Ying Xu,Xusheng Jiang et al.
Feifei Shen et al.
Objective: To develop and evaluate machine learning models combined with survival analysis for predicting 7-, 14-, and 28-day mortality in critically ill children with acute kidney injury (AKI), identifying key predictors...
Beyond NT-proBNP and troponin: How machine learning redefines light-chain cardiac amyloidosis risk assessment [0.03%]
NT-proBNP和肌钙蛋白之外：机器学习如何重新定义轻链心脏淀粉样变性风险评估
Danni Wu,Xiaohang Liu,Xinhao Li et al.
Danni Wu et al.
Objective: To develop and validate a machine learning-based prognostic model that provides enhanced risk stratification for AL cardiac amyloidosis patients beyond existing staging system. ...
Dynamic survival analysis via a landmarking-gradient boosting approach and its application to kidney transplant data [0.03%]
基于路标位点的提升梯度法在生存分析中的应用及肾移植数据的实证研究
Niloofar Shabani,Mehdi Yaseri,Rasoul Alimi et al.
Niloofar Shabani et al.
Background: In some survival studies, longitudinal biomarkers, along with baseline covariates, play crucial roles in predicting patient survival. Dynamic prediction models that incorporate updated longitudinal marker info...
Development of a big data platform for collecting and utilizing clinical information from the Korea Biobank Network [0.03%]
韩国生物样本库网络临床信息的大数据平台建设
Yun Seon Im,Seol Whan Oh,Ki Hoon Kim et al.
Yun Seon Im et al.
Background: Advanced biobanks increasingly focus on supporting biomedical research through the collection and integration of large-scale biological and clinical datasets. This study aimed to develop a big data platform th...
Development and external validation of a machine learning-based predictive model for acute kidney injury in hospitalized children with idiopathic nephrotic syndrome [0.03%]
基于机器学习的预测模型在住院儿童原发性肾病综合征患者中急性肾损伤的应用开发及外部验证研究
Xuejun Yang,De Zhang,Yan Li et al.
Xuejun Yang et al.