Synthetic data generation in healthcare: A scoping review of reviews on domains, motivations, and future applications [0.03%]
医疗保健中的合成数据生成：领域、动机和未来应用的综述性评论的范围审查
Miguel Rujas,Rodrigo Martín Gómez Del Moral Herranz,Giuseppe Fico et al.
Miguel Rujas et al.
Background: The development of Artificial Intelligence in the healthcare sector is generating a great impact. However, one of the primary challenges for the implementation of this technology is the access to high-quality ...
A meta-analysis of AI and machine learning in project management: Optimizing vaccine development for emerging viral threats in biotechnology [0.03%]
基于人工智能和机器学习的项目管理元分析：优化生物技术领域新兴病毒威胁下的疫苗开发
Jatin Vaghasiya,Mahim Khan,Tarak Milan Bakhda
Jatin Vaghasiya
Objectives: Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies across various industries, including healthcare, biotechnology, and vaccine development. These technologies of...
OptimCLM: Optimizing clinical language models for predicting patient outcomes via knowledge distillation, pruning and quantization [0.03%]
基于知识蒸馏、剪枝和量化优化临床语言模型以预测患者预后（OptimCLM）
Mohammad Junayed Hasan,Fuad Rahman,Nabeel Mohammed
Mohammad Junayed Hasan
Background: Clinical Language Models (CLMs) possess the potential to reform traditional healthcare systems by aiding in clinical decision making and optimal resource utilization. They can enhance patient outcomes and help...
Persuasive strategies in digital interventions to combat internet addiction: A systematic review [0.03%]
用于防治网络成瘾的数字干预措施中的劝说策略：系统评价研究
Yansen Theopilus,Abdullah Al Mahmud,Hilary Davis et al.
Yansen Theopilus et al.
Background: The internet provides valuable benefits in supporting our lives. However, concerns arise regarding internet addiction, a behavioural disorder due to excessive and uncontrolled internet use that has harmful eff...
Unmasking the chameleons: A benchmark for out-of-distribution detection in medical tabular data [0.03%]
揭穿变色龙：医学表格数据中异常检测的基准测试
Mohammad Azizmalayeri,Ameen Abu-Hanna,Giovanni Cinà
Mohammad Azizmalayeri
Background: Machine Learning (ML) models often struggle to generalize effectively to data that deviates from the training distribution. This raises significant concerns about the reliability of real-world healthcare syste...
Modeling the fasting blood glucose response to basal insulin adjustment in type 2 diabetes: An explainable machine learning approach on real-world data [0.03%]
基于真实世界数据的可解释机器学习方法模拟2型糖尿病患者基础胰岛素调整对空腹血糖的影响
Camilla Heisel Nyholm Thomsen,Thomas Kronborg,Stine Hangaard et al.
Camilla Heisel Nyholm Thomsen et al.
Introduction: Optimal basal insulin titration for people with type 2 diabetes is vital to effectively reducing the risk of complications. However, a sizeable proportion of people (30-50 %) remain in suboptimal glycemic co...
Training machine learning models to detect rare inborn errors of metabolism (IEMs) based on GC-MS urinary metabolomics for diseases screening [0.03%]
基于GC-MS尿代谢组学训练机器学习模型筛查罕见遗传元性疾病(IEMs)
Haomin Li,Siyuan Gao,Dan Wu et al.
Haomin Li et al.
Background: Gas chromatography-mass spectrometry (GC-MS) has been shown to be a potentially efficient metabolic profiling platform in urine analysis. However, the widespread use of GC-MS for inborn errors of metabolism (I...
Real-time assistance in suicide prevention helplines using a deep learning-based recommender system: A randomized controlled trial [0.03%]
基于深度学习的推荐系统在自杀预防热线中的实时辅助：一项随机对照试验
Salim Salmi,Saskia Mérelle,Nikki van Eijk et al.
Salim Salmi et al.
Objective: To evaluate the effectiveness and usability of an AI-assisted tool in providing real-time assistance to counselors during suicide prevention helpline conversations. ...
Active learning for extracting rare adverse events from electronic health records: A study in pediatric cardiology [0.03%]
主动学习在儿科心脏病电子健康记录中提取罕见不良事件中的应用研究
Sophie Quennelle,Sophie Malekzadeh-Milani,Nicolas Garcelon et al.
Sophie Quennelle et al.
Objective: Automate the extraction of adverse events from the text of electronic medical records of patients hospitalized for cardiac catheterization. Met...
A novel approach to antimicrobial resistance: Machine learning predictions for carbapenem-resistant Klebsiella in intensive care units [0.03%]
一种针对抗微生物药物耐药性的新方法：重症监护病房中碳青霉烯类抗生素耐药肠杆菌科机器学习预测模型
V Alparslan,Ö Güler,B İnner et al.
V Alparslan et al.
This study was conducted at Kocaeli University Hospital in Turkey and aimed to predict carbapenem-resistant Klebsiella pneumoniae infection in intensive care units using the Extreme Gradient Boosting (XGBoost) algorithm, a form of artificia...