Leveraging ChatGPT for thematic analysis of medical best practice advisory data [0.03%]
利用ChatGPT进行医学最佳实践咨询数据主题分析
Yejin Jeong,Margaret Smith,Robert J Gallo et al.
Yejin Jeong et al.
Objectives: To evaluate ChatGPT's ability to perform thematic analysis of medical Best Practice Advisory (BPA) free-text comments and identify prompt engineering strategies that optimize performance. ...
Development of a risk factor framework to inform machine learning prediction of young people's mental health problems: a Delphi study [0.03%]
一项关于青年心理问题的机器学习预测风险因素框架的发展：德尔菲研究
Katherine Parkin,Ryan Crowley,Rachel Sippy et al.
Katherine Parkin et al.
Objectives: To create a theoretical framework of mental health risk factors to inform the development of prediction models for young people's mental health problems. ...
Higher electronic health record burden among women physicians in academic ambulatory medicine [0.03%]
学术门诊中女性医师较高的电子健康档案负担
Sarah Y Bessen,Sean Tackett,Kimberly S Peairs et al.
Sarah Y Bessen et al.
Objectives: Electronic health record (EHR) work may differently affect women and men physicians. Identifying gender discrepancies in EHR work across different specialties may inform strategies to reduce EHR burdens. ...
Correction to: Response to survey directed to patient portal members differs by age, race, and healthcare utilization [0.03%]
对面向患者门户网站成员调查回复的差异与年龄、种族和医疗保健利用情况有关——对“患者门户网站成员调查回复的差异与年龄、种族和医疗保健利用情况有关”的回应
[This corrects the article DOI: 10.1093/jamiaopen/ooz061.]. © The Author(s) 2025. Published by Oxford University Press on behalf of the American Medical...
Published Erratum
JAMIA open. 2025 Dec 10;8(6):ooaf124. DOI:10.1093/jamiaopen/ooaf124 2025
Exploring common data model coverage of nursing flowsheet data: a pilot study using SNOMED CT and LOINC mapping [0.03%]
使用SNOMED CT和LOINC映射探索护理流式数据通用数据模型覆盖率：一项试点研究
Robin Austin,Malin Britt Lalich,Katy Stewart et al.
Robin Austin et al.
Objectives: The primary objective of this research is to assess the content coverage of nursing data within a publicly available common data model (CDM), focusing on how nursing data, documented in flowsheets, are represe...
Utilizing natural language processing to identify cancer-relevant publications at a National Cancer Institute-designated cancer center [0.03%]
利用自然语言处理技术识别国家癌症研究所指定的肿瘤医学中心的相关出版物
Whitney Shae,Md Saiful Islam Saif,John Fife et al.
Whitney Shae et al.
Objectives: The objective of this study was to develop and test natural language processing (NLP) methods for screening and, ultimately, predicting the cancer relevance of peer-reviewed publications. ...
Automated classification of exposure and encourage events in speech data from pediatric OCD treatment [0.03%]
儿童OCD治疗讲话数据中暴露和鼓励事件的自动分类
Juan Antonio Lossio-Ventura,Samuel Frank,Grace Ringlein et al.
Juan Antonio Lossio-Ventura et al.
Objective: To develop and evaluate an automated classification system for labeling Exposure Process Coding System (EPCS) quality codes-specifically exposure and encourage events-during in-person exposure therapy sessions ...
Multimodal feature analysis for automated neonatal jaundice assessment using machine learning [0.03%]
基于机器学习的自动化新生儿黄疸评估的多模态特征分析
Yunfeng Liang,Lin Zou,Millie Ming Rong Goh et al.
Yunfeng Liang et al.
Objective: Neonatal jaundice monitoring is resource-intensive. Existing artificial intelligence methods use image or clinical data, but none systematically combine both or compare feature contributions. This study fills t...
Using machine learning algorithms to optimize treatment with high-cost biologics in a national cohort of patients with inflammatory bowel disease [0.03%]
利用机器学习算法优化炎症性肠病全国患者队列的高成本生物制剂治疗
Jason K Hou,Tiffany M Tang,Shubhada Sansgiry et al.
Jason K Hou et al.
Objectives: Prediction models using statistical or machine learning (ML) approaches can enhance clinical decision support tools. Infliximab (IFX), a biologic with a newly introduced biosimilar for Crohn's disease (CD) and...
Evaluating sociodemographic bias in a deployed machine-learned patient deterioration model [0.03%]
评估已部署的机器学习患者病情恶化模型中的社会人口统计学偏见
Michael Colacci,Chloe Pou-Prom,Arjumand Siddiqi et al.
Michael Colacci et al.
Background: Bias evaluations of machine learning (ML) models often focus on performance in research settings, with limited assessment of downstream bias following clinical deployment. The objective of this study was to ev...