Explainable machine learning in healthcare: methods, interpretation, and applications for clinical research [0.03%]
医疗保健中的可解释机器学习：方法、解释和临床研究应用
Krishna Padmanabhan,Minxin Lu,Dai Feng et al.
Krishna Padmanabhan et al.
Objectives: To provide a practical and methodologically grounded overview of explainable machine learning (XML) approaches in healthcare, with emphasis on their interpretation and application in clinical research and deci...
Explainability in context: calibrating appropriate trust and reliance in artificial intelligence [0.03%]
解释性问题中的适当信任与依赖校准
Sharon E Davis,Megan E Salwei
Sharon E Davis
Background and significance: Predictive artificial intelligence (AI) promises to transform care delivery, enhance patient safety, and improve health outcomes. Realizing these benefits will require careful design, implemen...
Suzanne Bakken
Suzanne Bakken
Building safer artificial intelligence mental health chatbots: a framework for transparency, evaluation, and shared accountability [0.03%]
构建更安全的人工智能心理健康聊天机器人：透明度、评估和共同责任框架
Hannah Lee,Rebecca Handler,Tushar Mungle et al.
Hannah Lee et al.
Background: Generative artificial intelligence (AI) chatbots built on large language models are rapidly entering mental-health care, offering human-like support without meeting evidentiary standards for safety or effectiv...
Fine-tuning and evaluating large language models for patient safety tasks: classification of contributing factors in incident reports [0.03%]
大规模语言模型在患者安全任务中的微调与评估：事件报告中促成因素的分类
Ying Wang,Lorelle Bowditch,Charlotte Molloy et al.
Ying Wang et al.
Objective: To evaluate and compare the performance of large language models (LLMs) in identifying contributing factors (CFs) underlying patient safety incident investigations. ...
Alert fatigue measurement in clinical decision support: a systematic review [0.03%]
警觉疲劳在临床决策支持中的测量：系统回顾研究
Cara E Ray,Geneva M Wilson,Ashley M Hughes et al.
Cara E Ray et al.
Background: Alert fatigue is defined as alert dismissals due to excessive or irrelevant alerts and is frequently cited as a barrier to clinical decision support system use and impact. However, the criteria for determining...
Harnessing institutional knowledge: mixed methods evaluation of peer coaching in a multi-site EHR transition [0.03%]
利用机构知识进行同行辅导的混合方法评价——以多地点电子健康记录转换为例
Julian Brunner,Ryan Sterling,Ekaterina Cole et al.
Julian Brunner et al.
Objective: Health systems undertaking electronic health record (EHR) transitions often struggle to prepare and support clinicians in learning and using the new system. We evaluated a national peer coaching program-the Nat...
Generative artificial intelligence for inpatient documentation summarization: mixed-methods quality assessment and early real-world experience [0.03%]
生成式人工智能在住院文档摘要中的质量评估及早期实际应用体验：混合研究方法
Steve G Peters,Jens P Boyum,Sean R Legler et al.
Steve G Peters et al.
Objective: We evaluated the quality and adoption of a large language model (LLM)-based summarization tool for ongoing hospital care. Materials and methods...
Loss function influence on hyperparameter optimization for observational healthcare prediction models [0.03%]
损失函数对观察型医疗保健预测模型超参数优化的影响
Fleur Vereijken,Jenna M Reps,Peter Rijnbeek et al.
Fleur Vereijken et al.
Objectives: Prediction models are increasingly used in healthcare for risk stratification and personalized care. Many models are developed using machine learning, which requires tuning hyperparameters to maximize performa...