Validation of 13 102 International Classification of Diseases, Tenth Revision, Clinical Modification codes using a large language model-based system [0.03%]
使用大型语言模型基于系统验证国际疾病分类第十版临床修订本代码有效性(13-10-2)
Yichen Wang,Yilin Song,Rex Siu et al.
Yichen Wang et al.
Objectives: To comprehensively evaluate the validity of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for both prevalent diagnoses and less common diseases, and to asses...
Interviews with clinicians about an ambient artificial intelligence documentation platform [0.03%]
关于临床人员使用环境人工智能文档平台访谈的研究
Cheryl D Stults,Meghan C Martinez,Nina K Szwerinski et al.
Cheryl D Stults et al.
Objective: Understand the qualitative impact of an ambient artificial intelligence (AI) documentation platform on clinicians' experiences and workflows. M...
Causal modeling of chronic kidney disease in a participatory framework for informing the inclusion of social drivers in health algorithms [0.03%]
参与式框架中的慢性肾脏疾病因果模型构建：将社会因素纳入医疗算法的考量
Agata Foryciarz,Neha Srivathsa,Oshra Sedan et al.
Agata Foryciarz et al.
Objectives: Incomplete or incorrect causal theories are a key source of bias in machine learning (ML) algorithms. Community-engaged methodologies provide an avenue for mitigating this bias through incorporating causal ins...
Development of BERT-based large language models for emergency department triage using real-world conversations [0.03%]
基于BERT的大规模语言模型在急诊分诊中的开发和应用真实对话数据
Sukyo Lee,Sumin Jung,Jong-Hak Park et al.
Sukyo Lee et al.
Objectives: Accurate triage in emergency departments (ED) is critical for appropriate resource allocation. While artificial intelligence (AI) has been explored for triage, prior models relied on summarized clinical scenar...
The subtleties of abolishing "race correction" in clinical artificial intelligence [0.03%]
临床人工智能中废除“种族矫正”的细微之处
Moustafa Abdalla,LLana James,David S Jones et al.
Moustafa Abdalla et al.
Objectives: To explore the complexities of eliminating race correction in clinical artificial intelligence (AI), the pitfalls of naive solutions, and to propose systematic strategies for equitable model development. ...
Translating evidence into practice: adapting TrialGPT for real-world clinical trial eligibility screening [0.03%]
基于TrialGPT的真实世界临床试验入组筛查适配性研究
Mahanazuddin Syed,Muayad Hamidi,Manju Bikkanuri et al.
Mahanazuddin Syed et al.
Objectives: To evaluate the performance of a locally deployed adaptation of TrialGPT, a large language model (LLM) system for identifying trial-eligible patients from unstructured electronic health record (EHR) data. ...
NutriRAG: unleashing the power of large language models for food identification and classification through retrieval methods [0.03%]
NutriRAG：通过检索方法释放大型语言模型在食品识别和分类中的潜力
Huixue Zhou,Lisa Chow,Lisa Harnack et al.
Huixue Zhou et al.
Objectives: This study explores the use of advanced natural language processing (NLP) techniques to enhance food classification and dietary analysis using raw text input from a diet tracking app. ...
On embedding-based automatic mapping of clinical classification system: handling linguistic variations and granular inconsistencies [0.03%]
基于嵌入的临床分类系统自动映射：处理语言差异和粒度不一致性
Santosh Purja Pun,Oliver Obst,Jim Basilakis et al.
Santosh Purja Pun et al.
Objectives: Mapping clinical classification systems, such as the International Classification of Diseases (ICD), is essential yet challenging. While the manual mapping method remains labor-intensive and lacks scalability,...
From use cases to infrastructure: a cross-institutional survey of priorities in data-driven biomedical research [0.03%]
从用例到基础设施：数据驱动的生物医学研究中各机构优先级的交叉调查
Raja Mazumder,Jonathon Keeney,Luke Johnson et al.
Raja Mazumder et al.
Objectives: Federated Ecosystems for Analytics and Standardized Technologies (FEAST) is a modular, cloud-based platform developed through the ARPA-H Biomedical Data Fabric initiative to enable secure, federated analysis o...