Physiological foundation modeling for subclinical disease assessment: a prospective pilot [0.03%]
亚临床疾病评估的生理基础模型化：前瞻性试验
William Yuan,Shiwei Xu,Sara Dionisi et al.
William Yuan et al.
Objective: Clinical studies struggle to locate the right patients, in part because many remain undiagnosed or lack relevant labels. We develop and prospectively test a physiology-based patient representation ("Bioprofile"...
Improving medication error classification using a reasoning large language model [0.03%]
使用推理型大型语言模型改进药物错误分类
Anders Krifors,Theodor Beskow,Magnus Jonsson et al.
Anders Krifors et al.
Objectives: To assess the performance of a reasoning large language model (LLM) in identifying medication errors in medical incident reports. Materials an...
Case Reports
JAMIA open. 2026 Jan 24;9(1):ooag004. DOI:10.1093/jamiaopen/ooag004 2026
People, process, technology: a framework for clinical informatics fellowship applicants to evaluate programs [0.03%]
以人为本，以流程为导，以技术为支撑：一个临床信息学培训项目的评估框架
Jared Silberlust,Priyanka Solanki,Jonathan Austrian et al.
Jared Silberlust et al.
Objectives: To propose a structured framework for evaluating and comparing clinical informatics fellowship programs using the People, Process, and Technology (PPT) model. ...
EmergInsight: a real-time dashboard for optimizing emergency care through data visualization and analytics [0.03%]
EmergInsight：一个通过数据可视化与分析来优化急诊护理的实时控制面板
Francesco Branda,Vincenzo Andretta,Mohamed Mustaf Ahmed et al.
Francesco Branda et al.
Objectives: The fragmentation of healthcare data in Italy and the increase in demand in emergency departments (EDs) require innovative solutions to improve operational flows, quality of care, and decision-making. This stu...
MedSlice: fine-tuned large language models for secure clinical note sectioning [0.03%]
MedSlice：用于安全临床笔记分区的精细调整大型语言模型
Joshua Davis,Thomas Sounack,Kate Sciacca et al.
Joshua Davis et al.
Objectives: Extracting sections from clinical notes is crucial for downstream analysis but is challenging due to variability in formatting and labor-intensive nature of manual sectioning. This study develops a pipeline fo...
Lightweight open-source large language models versus cTAKES for information extraction from discharge summaries: tobacco smoking status test case [0.03%]
轻量级开源大型语言模型与cTAKES在出院总结信息抽取中的对比：以烟草使用情况为例
David M Dávila-García,Matthew J Schuelke,Adam B Wilcox
David M Dávila-García
Objectives: To compare lightweight open-source large language models (LLMs) with cTAKES, a state-of-the-art natural language processing (NLP) system, in an information extraction task from hospitalization discharge summar...
Fairness-aware K-means clustering in digital mental health for higher education students: a generalizable framework for equitable clustering [0.03%]
一种面向高等教育学生的数字心理健康公平感知K-均值聚类方法：用于公平聚类的可推广框架
Priyanshu Alluri,Zequn Chen,Thomas Thesen et al.
Priyanshu Alluri et al.
Objectives: Higher education students, particularly those from underrepresented backgrounds, experience heightened levels of anxiety, depression, and burnout. Clinical informatics approaches leveraging K-means clustering ...
Better than nothing, far from perfect: hospital and healthcare system leaders' perspectives on health information exchanges [0.03%]
任重道远：健康信息交换在医院和卫生系统领导眼中的利与弊分析
Sara D Turbow,Camille P Vaughan,Mohammed K Ali et al.
Sara D Turbow et al.
Objectives: Implementation and effective use of health information exchange (HIE) has the potential to transform health care and improve patient outcomes across the United States, yet HIEs remain underutilized nationally....
Preliminary insights into artificial intelligence guided dosing in hypertension and diabetes: challenges and lessons learnt in a pilot feasibility study [0.03%]
人工智能指导下的高血压和糖尿病剂量调整的初步见解：试点可行性研究中的挑战与经验教训
Jennifer Sumner,Mehul Motani,Jaminah Mohamed Ali et al.
Jennifer Sumner et al.
Objective: CURATE.AI is an artificial intelligence platform enabling personalised drug dosing. Aims:1) Determine the feasibility of using CURATE.AI in the outpatient setting.2) Compare the consistency of CURATE.AI recomme...
Inferring high-fat dietary patterns from electronic health record data using machine learning [0.03%]
基于电子健康记录数据使用机器学习推断高脂肪的饮食模式
Ya-Yun Yeh,Hsin-Yueh Lin,Jingchuan Guo et al.
Ya-Yun Yeh et al.
Objectives: Electronic health records (EHRs) rarely capture dietary detail, limiting diet-disease research. We aimed to develop machine learning (ML) computable phenotypes to identify high-fat diet (HFD) using variables t...