Contrasting attitudes towards current and future artificial intelligence applications for computerised interpretation of electrocardiograms: a clinical stakeholder interview study [0.03%]
利益相关者访谈研究对比临床医生对现有人工智能和未来人工智能解释心电图的态度
Lukas Hughes-Noehrer,Leda Channer,Gabriel Strain et al.
Lukas Hughes-Noehrer et al.
Objectives: To investigate clinicians' attitudes towards current automated interpretation of ECG and novel AI technologies and their perception of computer-assisted interpretation. ...
Current challenges and opportunities in active and passive data collection for mobile health sensing: a scoping review [0.03%]
移动健康传感主动和被动数据收集的当前挑战与机遇：综述研究
Christopher Slade,Yinan Sun,Wei Cheng Chao et al.
Christopher Slade et al.
Objective: Mobile and ubiquitous devices enable health data collection "in a free-living environment" to support applications such as remote patient monitoring and adaptive digital interventions using machine learning (ML...
An open-source, automated machine learning approach for large-scale image retrieval for thoracic aorta analysis studies [0.03%]
用于主动脉分析研究的大规模图像检索的开源自动机器学习方法
Brian C Ayers,Aaron D Aguirre,Thoralf M Sundt et al.
Brian C Ayers et al.
Objective: To develop an image retrieval pipeline capable of identifying specific series of thoracic aortic computed tomography (CT) scans from a diverse database. ...
Patrycja Krawczuk,Zachary R Fox,Valentina Petkov et al.
Patrycja Krawczuk et al.
Objectives: No existing algorithm can reliably identify metastasis from pathology reports across multiple cancer types and the entire US population. In this study, we develop a deep learning model that automatically detec...
A study of calibration as a measurement of trustworthiness of large language models in biomedical natural language processing [0.03%]
一种校准研究作为测量生物医学自然语言处理中大型语言模型可靠性的方法
Rodrigo de Oliveira,Matthew Garber,James M Gwinnutt et al.
Rodrigo de Oliveira et al.
Objectives: To assess the calibration of 9 large language models (LLMs) within biomedical natural language processing (BioNLP) tasks, furthering understanding of trustworthiness and reliability in real-world settings. ...
Utilizing Immuno-Oncology registry data for enhanced non-small cell lung cancer treatment predictions [0.03%]
利用免疫肿瘤学登记数据增强非小细胞肺癌治疗预测
Yili Zhang,Shaked Lev-Ari,Jacob Zaemes et al.
Yili Zhang et al.
Objectives: We aim to leverage more comprehensive phenotypic and genotypic clinical data to enhance the treatment response predictions. Materials and meth...
Integrating a risk prediction score in a clinical decision support to identify patients with health-related social needs in the emergency department [0.03%]
在急诊临床决策支持中整合风险预测评分以识别有健康相关社会需求的患者
Olena Mazurenko,Christopher A Harle,Paul I Musey Jr et al.
Olena Mazurenko et al.
Objectives: To improve the identification of patients with health-related social needs (HRSNs) in the emergency department (ED), we developed and integrated a risk prediction score into an existing Fast Healthcare Interop...
The Community Vulnerability Compass: a novel, scalable approach for measuring and visualizing social determinants of health insights [0.03%]
社区脆弱性 compass：一种新颖可扩展的方法，用于衡量和可视化健康的社会决定因素的见解
Yolande Pengetnze,Venkatraghavan Sundaram,Yusuf Tamer et al.
Yolande Pengetnze et al.
Objectives: To determine whether a novel digital tool, the Community Vulnerability Compass (CVC), built using large datasets, can accurately measure neighborhood- and individual-level social determinants of health (SDOH) ...
Artificial intelligence models for predicting acute kidney injury in the intensive care unit: a systematic review of modeling methods, data utilization, and clinical applicability [0.03%]
重症监护病房急性肾损伤预测的人工智能模型系统评价：建模方法、数据利用及临床适用性
Tongyue Shi,Yu Lin,Huiying Zhao et al.
Tongyue Shi et al.
Objectives: Acute kidney injury (AKI) is common in intensive care unit (ICU) patients and is associated with high mortality, prolonged ICU stays, and increased costs. Early prediction is crucial for timely intervention an...
Conversational health agents: a personalized large language model-powered agent framework [0.03%]
对话式健康代理：一种个性化的大语言模型驱动的代理框架
Mahyar Abbasian,Iman Azimi,Amir M Rahmani et al.
Mahyar Abbasian et al.
Objective: Conversational Health Agents (CHAs) are interactive systems providing healthcare services, such as assistance and diagnosis. Current CHAs, especially those utilizing Large Language Models (LLMs), primarily focu...