Evaluating the Diagnostic Performance of Symptom Checkers: Clinical Vignette Study [0.03%]
在线自我诊断工具的症状检查准确性评测研究：临床案例研究
Mohammad Hammoud,Shahd Douglas,Mohamad Darmach et al.
Mohammad Hammoud et al.
Background: Medical self-diagnostic tools (or symptom checkers) are becoming an integral part of digital health and our daily lives, whereby patients are increasingly using them to identify the underlying causes of their ...
Identifying Links Between Productivity and Biobehavioral Rhythms Modeled From Multimodal Sensor Streams: Exploratory Quantitative Study [0.03%]
基于多模态传感器流建模的生产力与生物行为节奏之间关联的探索性量化研究
Runze Yan,Xinwen Liu,Janine M Dutcher et al.
Runze Yan et al.
Background: Biobehavioral rhythms are biological, behavioral, and psychosocial processes with repeating cycles. Abnormal rhythms have been linked to various health issues, such as sleep disorders, obesity, and depression....
Feasibility of Multimodal Artificial Intelligence Using GPT-4 Vision for the Classification of Middle Ear Disease: Qualitative Study and Validation [0.03%]
基于GPT-4视觉的多模态人工智能在中耳疾病分类中的可行性：定性研究与验证
Masao Noda,Hidekane Yoshimura,Takuya Okubo et al.
Masao Noda et al.
Background: The integration of artificial intelligence (AI), particularly deep learning models, has transformed the landscape of medical technology, especially in the field of diagnosis using imaging and physiological dat...
Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling [0.03%]
一种对有限的、多模态的和纵向的行为被动感知数据进行机器学习预测排名的框架：结合用户无关模型和个人化模型
Tahsin Mullick,Sam Shaaban,Ana Radovic et al.
Tahsin Mullick et al.
Background: Passive mobile sensing provides opportunities for measuring and monitoring health status in the wild and outside of clinics. However, longitudinal, multimodal mobile sensor data can be small, noisy, and incomp...
Understanding the Long Haulers of COVID-19: Mixed Methods Analysis of YouTube Content [0.03%]
了解COVID-19长期感染者的状况：YouTube内容的混合方法分析
Alexis Jordan,Albert Park
Alexis Jordan
Background: The COVID-19 pandemic had a devastating global impact. In the United States, there were >98 million COVID-19 cases and >1 million resulting deaths. One consequence of COVID-19 infection has been post-COVID-19 ...
Adolescents' Well-being While Using a Mobile Artificial Intelligence-Powered Acceptance Commitment Therapy Tool: Evidence From a Longitudinal Study [0.03%]
纵向研究中关于青少年使用移动人工智能赋能的接纳承诺疗法工具的心理健康状况证据
Dana Vertsberger,Navot Naor,Mirène Winsberg
Dana Vertsberger
Background: Adolescence is a critical developmental period to prevent and treat the emergence of mental health problems. Smartphone-based conversational agents can deliver psychologically driven intervention and support, ...
Improving Risk Prediction of Methicillin-Resistant Staphylococcus aureus Using Machine Learning Methods With Network Features: Retrospective Development Study [0.03%]
基于网络特征运用机器学习方法改进 meticillin 抵抗金黄色葡萄球菌的风险预测：回顾性研究
Methun Kamruzzaman,Jack Heavey,Alexander Song et al.
Methun Kamruzzaman et al.
Background: Health care-associated infections due to multidrug-resistant organisms (MDROs), such as methicillin-resistant Staphylococcus aureus (MRSA) and Clostridioides difficile (CDI), place a significant burden on our ...
Beyond the Hype-The Actual Role and Risks of AI in Today's Medical Practice: Comparative-Approach Study [0.03%]
超越炒作——人工智能在当今医学实践中的实际作用和风险：比较方法研究
Steffan Hansen,Carl Joakim Brandt,Jens Søndergaard
Steffan Hansen
Background: The evolution of artificial intelligence (AI) has significantly impacted various sectors, with health care witnessing some of its most groundbreaking contributions. Contemporary models, such as ChatGPT-4 and M...
Generating Synthetic Electronic Health Record Data Using Generative Adversarial Networks: Tutorial [0.03%]
使用生成对抗网络生成合成电子健康记录数据：教程
Chao Yan,Ziqi Zhang,Steve Nyemba et al.
Chao Yan et al.
Synthetic electronic health record (EHR) data generation has been increasingly recognized as an important solution to expand the accessibility and maximize the value of private health data on a large scale. Recent advances in machine learni...