Large language model-based extraction of rheumatoid arthritis clinical disease activity index from clinical notes [0.03%]
基于大型语言模型的类风湿关节炎临床疾病活动指数的提取
Reid Weisberg,Ruoqi Yang,Iram Kamdar et al.
Reid Weisberg et al.
Objectives: We extracted a validated disease activity measure in rheumatoid arthritis (RA), the Clinical Disease Activity Index (CDAI), from a large tertiary academic medical center electronic health record (EHR) using an...
From fragmented records to living evidence: health system-governed, artificial intelligence-driven, continuously updated real-world clinical data from Truveta [0.03%]
从碎片化记录到活证据：Truveta的健康系统治理、人工智能驱动和持续更新的真实世界临床数据
Hannah A Burkhardt,Sarah J Blach,Srinivasa R Burugapalli et al.
Hannah A Burkhardt et al.
Objectives: Real-world data (RWD) have historically suffered from fragmentation, delayed availability, variable data quality, and limited analytic utility. Truveta developed an artificial intelligence (AI)-enabled data pl...
Sequential multi-site fine-tuning for incremental deployment of large language models for mobility functional status extraction [0.03%]
序列多站点微调技术在大规模语言模型移动功能状态提取中的增量部署应用研究
Xingyi Liu,Muskan Garg,Eunji Jeon et al.
Xingyi Liu et al.
Objective: This study evaluated sequential multi-site fine-tuning of large language models (LLMs), simulating incremental deployment across institutions for extracting mobility functional status from unstructured clinical...
Challenges implementing treatment guidelines in electronic health records: the American Diabetes Association Standards of Care as a case example [0.03%]
电子健康记录实施治疗指南的挑战——美国糖尿病协会护理标准为例
Irene Demarie,Michael E Bowen,Shubham Agarwal et al.
Irene Demarie et al.
Objective: Treatment guidelines can improve population health; however, their implementation within electronic health records (EHRs) can be challenging. We aimed to create an implementable framework using the American Dia...
Understanding end-user contexts and identifying design preferences of an artificial intelligence-based clinical decision support tool for early autism detection [0.03%]
理解终端用户上下文并识别基于人工智能的临床决策支持工具（用于自闭症早期检测）的设计偏好
Adesuwa Emovon,Lauren Driggers-Jones,Matthew Engelhard et al.
Adesuwa Emovon et al.
Objectives: Building on innovations for autism detection-where artificial intelligence (AI)-based models monitor clinical data within electronic health records-this study evaluates the context for clinical decision suppor...
Machine learning on asynchronous clinical pages to predict clinical deterioration [0.03%]
基于非实时临床数据页面的机器学习预测临床病情恶化
Isabel C Arvelo,Kipp Shipley,Adam Wright et al.
Isabel C Arvelo et al.
Objectives: Clinical deterioration in hospitalized patients is often preventable, but traditional early warning scores based on structured data are limited by delayed or inconsistent documentation. We developed and evalua...
Development of a clinical trial knowledge management application for community oncology [0.03%]
社区肿瘤学临床试验知识管理应用程序的开发
Elizabeth O&#x;Brien,Aanshi Thumar,Melanie Manning et al.
Elizabeth O&#x;Brien et al.
Introduction: Participation in cancer clinical trials is low in community oncology settings, partly because institution-specific trial information is fragmented. We evaluated feasibility of embedding curated trial content...
From clinics to discoveries: harnessing national EHR interoperability for research [0.03%]
从门诊到发现：利用全国电子健康记录互操作性进行研究
Josh Lemieux,Emily R Pfaff,Richard A Moffitt et al.
Josh Lemieux et al.
Objectives: To assess whether existing nationwide Health Information Networks (HINs) and Health Information Exchanges (HIEs) can be leveraged for electronic health record (EHR) data acquisition for research-specifically, ...
Development and validation of an algorithm to identify severe sepsis onset from electronic medical records [0.03%]
基于电子病历的严重脓毒症发病时间识别算法的构建及验证
Ramin Homayouni,Shane Morrell,Joel David Karsten et al.
Ramin Homayouni et al.
Objective: To develop and evaluate an automated algorithm to identify sepsis onset, referred to as time-zero (t0), from the electronic medical records (EMR). ...
DETERIO-LLM: enhancing traditional deterioration risk scores with clinical context and advanced reasoning [0.03%]
基于临床背景和先进推理增强传统损伤风险评分的DETERIO-LLM模型
Ghodsieh Ghanbari,Joseph C Ahn,Eileen Kim et al.
Ghodsieh Ghanbari et al.
Objectives: Accurate and timely prediction of physiological deterioration in hospitalized patients is essential, but existing deep learning models, which rely solely on structured electronic health record (EHR) data, gene...