Theory and practice in biomedical informatics: a framework for discovery [0.03%]
生物医学信息学中的理论与实践：一种探索框架
William W Stead,Constantin F Aliferis,Lisa Bastarache et al.
William W Stead et al.
Objective: Clarify disciplinary foundations and internal structure of biomedical informatics. Methods: We analyze BMI's emergence at di...
SeqBoard: a genomics-based data dashboard for comprehensive wastewater virome monitoring [0.03%]
基于基因组的综合性污水病毒组监测数据仪表板SeqBoard
Cici Bauer,Nicholas Reger,Haider A L Rustem et al.
Cici Bauer et al.
Objectives: To develop the first public-facing dashboard that translates genomic sequencing data from wastewater into accessible and actionable community information concerning human pathogenic viruses, representing a shi...
Optimizing Retrieval-Augmented Generation (RAG) in clinical medicine: methods and performance evaluation [0.03%]
临床医学中检索增强生成（RAG）的优化：方法与性能评估
Pengze Li,Anshum Patel,Sai Krishna Vallamchetla et al.
Pengze Li et al.
Objective: Evaluate how RAG architecture, including corpus structure, retrieval strategy, and pipeline complexity, affects LLM-based medical problem solving and knowledge retrieval in sleep medicine. ...
Characterizing surgeon workload with electronic health record data to predict time interval between surgeries and postoperative care delivery [0.03%]
基于电子健康档案数据表征手术工作量以预测手术间隔时间及术后护理提供时间
Jonathan Akhagbosu,Muge Capan,Hari Balasubramanian et al.
Jonathan Akhagbosu et al.
Objectives: This study positions surgeon gap time, defined as the interval between consecutive surgeries performed by the same surgeon, as a surgeon-level metric of efficiency. Understanding gap time requires accounting f...
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. ...