Automating inductive thematic analyses of health content using large language models: a proof-of-concept study using social media data [0.03%]
基于社交媒体数据使用大规模语言模型自动化归纳主题分析的实证研究
JaMor Hairston,Ritvik Ranjan,Sahithi Lakamana et al.
JaMor Hairston et al.
Objectives: Large language models (LLMs) face challenges in inductive thematic analysis, a task requiring deep interpretive, domain-specific expertise. We evaluated the feasibility of using LLMs to replicate expert-driven...
A natural language processing pipeline for identifying pediatric long COVID symptoms and functional impacts in freeform clinical notes: a RECOVER study [0.03%]
一种自然语言处理流程，用于识别儿科长期新冠肺炎症状和功能影响在自由形式的临床记录中的情况：来自恢复计划的一项研究
H Timothy Bunnell,Cara Reedy,Vitaly Lorman et al.
H Timothy Bunnell et al.
Objective: To develop a natural language processing (NLP) pipeline for unstructured electronic health record (EHR) data to identify symptoms and functional impacts associated with Long COVID in children. ...
Data harmonization framework for neonatal hypoxic-ischemic encephalopathy studies [0.03%]
新生儿缺氧缺血性脑病研究的数据协调框架
Chuan-Heng Hsiao,Anna N Foster,Scott A McDonald et al.
Chuan-Heng Hsiao et al.
Objectives: To develop a data harmonization framework for neonatal hypoxic-ischemic encephalopathy (HIE) studies and demonstrate its suitability for prognostic biomarker development. ...
Generative artificial intelligence for automated data extraction from unstructured medical text [0.03%]
生成式人工智能在非结构化医学文本自动数据提取中的应用
Nam Dao,Luisa Quesada,Syed Moin Hassan et al.
Nam Dao et al.
Objectives: Unstructured data, such as procedure notes, contain valuable medical information that is frequently underutilized due to the labor-intensive nature of data extraction. This study aims to develop a generative a...
Documentation of social determinants of health for patients with type 2 diabetes in Epic Cosmos [0.03%]
Epic Cosmos 2型糖尿病患者的社会决定因素文档记录
Polina V Kukhareva,Matthew J O&#x;Brien,Daniel C Malone et al.
Polina V Kukhareva et al.
Objectives: Type 2 diabetes (T2D) is a growing public health burden with persistent racial and ethnic disparities. . This study assessed the completeness of social determinants of health (SdoH) data for patients with T2D ...
Artificial intelligence-generated encounter summaries: early insights from ambulatory clinicians at a large academic health system [0.03%]
学术健康系统门诊医师关于人工智能生成的就诊摘要的早期见解
Jared Silberlust,Priyanka Solanki,Elizabeth R Stevens et al.
Jared Silberlust et al.
Objectives: Assess time impact and provider perception of AI-generated encounter summaries. Materials and methods: An artificial intell...
Case Reports
JAMIA open. 2025 Sep 2;8(5):ooaf096. DOI:10.1093/jamiaopen/ooaf096 2025
Defining dyadic cancer pain concordance using participant-initiated interactions with a remote health monitoring system [0.03%]
基于参与者与远程健康监测系统交互定义癌痛一致性
Mina Ostovari,Natalie Crimp,Sarah J Ratcliffe et al.
Mina Ostovari et al.
Background: Studies on symptom concordance between patients and their caregivers often use cross-sectional designs, which may fail to capture the longitudinal, dynamic symptom experience. The Behavioral and Environmental ...
medspacyV: a graphical user interface for the open source medspaCy natural language processing package [0.03%]
MedSpaCy V: 具有图形用户界面的开源 MedSpaCy 自然语言处理软件包
Bharath Velamala,Elham Sagheb Hossein Pour,Michael Lin et al.
Bharath Velamala et al.
Objectives: To enable users with modest technical background to perform biomedical natural language processing (NLP). Materials and methods: ...
Assessing the advantages and disadvantages of dimensionality reduction methods in summarizing housing determinants of health in the United States [0.03%]
评估降维方法在美国住房健康决定因素汇总中的优劣
Xingyu Chen,Christopher Kitchen,Hadi Kharrazi
Xingyu Chen
Objectives: To evaluate and compare different dimensionality reduction techniques for quantifying housing conditions as a social determinant of health (SDOH) across various geographic levels in the United States. ...
EchoLLM: extracting echocardiogram entities with light-weight, open-source large language models [0.03%]
EchoLLM：使用轻量级开源大型语言模型提取心电图实体
Jonathan Chi,Yazan Rouphail,Ethan Hillis et al.
Jonathan Chi et al.
Objectives: Large language models (LLMs) have demonstrated high levels of performance in clinical information extraction compared to rule-based systems and traditional machine-learning approaches, offering scalability, co...