Using Conversational AI to Facilitate Mental Health Assessments and Improve Clinical Efficiency Within Psychotherapy Services: Real-World Observational Study [0.03%]
利用对话式人工智能促进心理健康评估和改善心理治疗服务临床效率的实地观察研究
Max Rollwage,Johanna Habicht,Keno Juechems et al.
Max Rollwage et al.
Background: Most mental health care providers face the challenge of increased demand for psychotherapy in the absence of increased funding or staffing. To overcome this supply-demand imbalance, care providers must increas...
Machine Learning-Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study [0.03%]
基于机器学习的时间模式在1型糖尿病管理中用于血糖波动模式识别：发展与验证研究
Nicholas Berin Chan,Weizi Li,Theingi Aung et al.
Nicholas Berin Chan et al.
Background: Continuous glucose monitoring (CGM) for diabetes combines noninvasive glucose biosensors, continuous monitoring, cloud computing, and analytics to connect and simulate a hospital setting in a person's home. CG...
Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study [0.03%]
基于大型多中心数据库的机器学习预测程序病例持续时间的研究：算法开发与验证研究
Samir Kendale,Andrew Bishara,Michael Burns et al.
Samir Kendale et al.
Background: Accurate projections of procedural case durations are complex but critical to the planning of perioperative staffing, operating room resources, and patient communication. Nonlinear prediction models using mach...
Identifying Symptoms Prior to Pancreatic Ductal Adenocarcinoma Diagnosis in Real-World Care Settings: Natural Language Processing Approach [0.03%]
基于自然语言处理的胰腺导管腺癌真实世界诊疗环境中诊断前症状识别研究
Fagen Xie,Jenny Chang,Tiffany Luong et al.
Fagen Xie et al.
Background: Pancreatic cancer is the third leading cause of cancer deaths in the United States. Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, accounting for up to 90% of all cases. ...
Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning-Based Natural Language Processing [0.03%]
基于深度学习的自然语言处理在肺癌患者肺部磨玻璃影特征自动化提取及纵向分析中的应用研究
Kyeryoung Lee,Zongzhi Liu,Urmila Chandran et al.
Kyeryoung Lee et al.
Background: Ground-glass opacities (GGOs) appearing in computed tomography (CT) scans may indicate potential lung malignancy. Proper management of GGOs based on their features can prevent the development of lung cancer. E...
Identifying Patterns of Smoking Cessation App Feature Use That Predict Successful Quitting: Secondary Analysis of Experimental Data Leveraging Machine Learning [0.03%]
利用机器学习杠杆进行实验数据的二次分析以识别预测戒烟成功的戒烟应用程序功能使用模式
Leeann Nicole Siegel,Kara P Wiseman,Alex Budenz et al.
Leeann Nicole Siegel et al.
Background: Leveraging free smartphone apps can help expand the availability and use of evidence-based smoking cessation interventions. However, there is a need for additional research investigating how the use of differe...
The Evolution of Artificial Intelligence in Biomedicine: Bibliometric Analysis [0.03%]
生物医学领域人工智能的 bibliometric 分析及演化趋势探究
Jiasheng Gu,Chongyang Gao,Lili Wang
Jiasheng Gu
Background: The utilization of artificial intelligence (AI) technologies in the biomedical field has attracted increasing attention in recent decades. Studying how past AI technologies have found their way into medicine o...
Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability Framework for Safe and Effective Large Language Models in Medical Education: Narrative Review and Qualitative Study [0.03%]
面向医学教育的安全且有效的大型语言模型的成本、可用性、可信度、公平性、问责制、透明性和可解释性框架：叙述性综述和质性研究
Majdi Quttainah,Vinaytosh Mishra,Somayya Madakam et al.
Majdi Quttainah et al.
Background: The world has witnessed increased adoption of large language models (LLMs) in the last year. Although the products developed using LLMs have the potential to solve accessibility and efficiency problems in heal...
Preparing for an Artificial Intelligence-Enabled Future: Patient Perspectives on Engagement and Health Care Professional Training for Adopting Artificial Intelligence Technologies in Health Care Settings [0.03%]
迎接人工智能时代的到来：患者视角下的参与和医疗保健专业人员的人工智能技术培训意见
Tharshini Jeyakumar,Sarah Younus,Melody Zhang et al.
Tharshini Jeyakumar et al.
Background: As new technologies emerge, there is a significant shift in the way care is delivered on a global scale. Artificial intelligence (AI) technologies have been rapidly and inexorably used to optimize patient outc...
Association of Health Care Work With Anxiety and Depression During the COVID-19 Pandemic: Structural Topic Modeling Study [0.03%]
新冠肺炎大流行期间医疗保健工作与焦虑和抑郁的相关性：结构主题模型研究
Matteo Malgaroli,Emily Tseng,Thomas D Hull et al.
Matteo Malgaroli et al.
Background: Stressors for health care workers (HCWs) during the COVID-19 pandemic have been manifold, with high levels of depression and anxiety alongside gaps in care. Identifying the factors most tied to HCWs' psycholog...