Targeted use of large language models for EHR-based computable phenotyping [0.03%]
大规模语言模型在基于电子健康记录的可计算表型分析中的靶向应用研究
Dylan Owens,Jing Cao,Mehak Gupta et al.
Dylan Owens et al.
Objective: Computable phenotypes derived from electronic health records (EHRs) are central to clinical research and quality reporting. Although large language models (LLMs) can extract clinically rich information from uns...
Applying natural language processing and large language models to clinical notes for phenotyping and diagnosing rare diseases: a systematic review [0.03%]
用于罕见病表型和诊断的临床注释自然语言处理和大模型应用：系统综述
Seungjun Kim,Yiliang Zhou,Yawen Guo et al.
Seungjun Kim et al.
Objectives: Patients with rare diseases often face long delays before receiving a diagnosis. Using electronic health records for automated phenotyping and diagnosis of rare diseases is a promising approach but can be chal...
Federated learning's uncomfortable truth: why human networks matter more than neural networks [0.03%]
联合学习的不舒适真相：为什么人类网络比神经网络更重要
Laura-Maria Peltonen,Taridzo Chomutare
Laura-Maria Peltonen
Objectives: To examine real-world barriers to implementing federated learning in healthcare and highlight the organizational, regulatory, and socio-technical factors often overlooked in technical research. ...
Opportunities for informatics to improve patient experiences: observations and reflections of ACMI fellows [0.03%]
临床医学信息学提高患者体验的机遇：ACMI成员们的见解与反思
Howard R Strasberg,Edward P Hoffer,Ross Koppel et al.
Howard R Strasberg et al.
Objectives: We report on findings from a meeting convened by the American College of Medical Informatics (ACMI) to characterize aspects of the patient experience that could be improved using informatics. ...
Automating infection indicator extraction in home healthcare through instruction-tuned large language models [0.03%]
通过指令微调的大语言模型自动化家庭护理中的感染指标提取
Zidu Xu,Jiyoun Song,Shuang Zhou et al.
Zidu Xu et al.
Objective: Home healthcare (HHC) clinical notes contain critical infection indicators that clinicians need in structured "indicator + context" pairs. Data sparsity and limited computing resources hinder automated extracti...
Electronic health record-based prediction models for dementia detection: a systematic review of model performance and quality [0.03%]
基于电子健康记录的痴呆检测预测模型系统评价：模型性能和质量评估
Alicia Lu,Velandai Srikanth,Sarah Westworth et al.
Alicia Lu et al.
Objectives: Leveraging routine electronic health records (EHR) for dementia detection is a growing field, but quality and clinical utility of existing models are unclear. This systematic review aimed to evaluate performan...
Leveraging clinical epidemiology concepts to strengthen machine learning fairness evaluations [0.03%]
利用临床流行病学概念加强机器学习公平性评估
Lin Lawrence Guo,Santiago E Arciniegas,Adam P Yan et al.
Lin Lawrence Guo et al.
Objectives: The increasing use of machine learning (ML) in clinical care makes fairness a central issue. Fairness, defined as the absence of disparities across individuals or subgroups, shares several parallels with conce...
A critical evaluation of generative query expansion on biomedical literature retrieval [0.03%]
生成式查询扩展在生物医学文献检索中的批判性评估
Yilu Fang,Gongbo Zhang,Fangyi Chen et al.
Yilu Fang et al.
Objective: To evaluate the effectiveness of generative query expansion for biomedical literature retrieval. Materials and methods: We t...
A systematic methodological review of best practices, pitfalls, and opportunities in mixed methods research in applied clinical informatics [0.03%]
应用临床信息学混合研究方法系统回顾最佳实践、陷阱及机遇
Oliver T Nguyen,Arsalan Ahmad,Michelle Doering et al.
Oliver T Nguyen et al.
Introduction: Mixed methods are used to holistically understand the "what", "how", and "why" questions within a single study by integrating quantitative and qualitative methods. Although this approach has demonstrated val...