Leveraging Temporal Trends for Training Contextual Word Embeddings to Address Bias in Biomedical Applications: Development Study [0.03%]
利用时间趋势训练上下文词嵌入以解决生物医学应用中的偏见：发展研究
Shunit Agmon,Uriel Singer,Kira Radinsky
Shunit Agmon
Background: Women have been underrepresented in clinical trials for many years. Machine-learning models trained on clinical trial abstracts may capture and amplify biases in the data. Specifically, word embeddings are mod...
Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study [0.03%]
数字化记录助手系统对临床文档的时间和质量的影响： usability研究
Marieke Meija van Buchem,Ilse M J Kant,Liza King et al.
Marieke Meija van Buchem et al.
Background: Physicians spend approximately half of their time on administrative tasks, which is one of the leading causes of physician burnout and decreased work satisfaction. The implementation of natural language proces...
Predictive Modeling of Hypertension-Related Postpartum Readmission: Retrospective Cohort Analysis [0.03%]
基于回顾性队列的高血压相关产后期再入院预测模型分析
Jinxin Tao,Ramsey G Larson,Yonatan Mintz et al.
Jinxin Tao et al.
Background: Hypertension is the most common reason for postpartum hospital readmission. Better prediction of postpartum readmission will improve the health care of patients. These models will allow better use of resources...
Development of Lung Cancer Risk Prediction Machine Learning Models for Equitable Learning Health System: Retrospective Study [0.03%]
面向公平的学习医疗系统的肺癌风险预测机器学习模型的开发：回顾性研究
Anjun Chen,Erman Wu,Ran Huang et al.
Anjun Chen et al.
Background: A significant proportion of young at-risk patients and nonsmokers are excluded by the current guidelines for lung cancer (LC) screening, resulting in low-screening adoption. The vision of the US National Acade...
Near Real-Time Syndromic Surveillance of Emergency Department Triage Texts Using Natural Language Processing: Case Study in Febrile Convulsion Detection [0.03%]
基于自然语言处理的急诊分诊文本近实时症候群监测：发热性惊厥检测案例研究
Sedigh Khademi,Christopher Palmer,Muhammad Javed et al.
Sedigh Khademi et al.
Background: Collecting information on adverse events following immunization from as many sources as possible is critical for promptly identifying potential safety concerns and taking appropriate actions. Febrile convulsio...
Obtaining the Most Accurate, Explainable Model for Predicting Chronic Obstructive Pulmonary Disease: Triangulation of Multiple Linear Regression and Machine Learning Methods [0.03%]
预测慢性阻塞性肺疾病的最准确、可解释的模型：多重线性回归和机器学习方法的三角测量
Arnold Kamis,Nidhi Gadia,Zilin Luo et al.
Arnold Kamis et al.
Background: Lung disease is a severe problem in the United States. Despite the decreasing rates of cigarette smoking, chronic obstructive pulmonary disease (COPD) continues to be a health burden in the United States. In t...
Traditional Machine Learning, Deep Learning, and BERT (Large Language Model) Approaches for Predicting Hospitalizations From Nurse Triage Notes: Comparative Evaluation of Resource Management [0.03%]
基于护士分诊记录预测住院的传统机器学习、深度学习和BERT（大型语言模型）方法的资源管理比较评估
Dhavalkumar Patel,Prem Timsina,Larisa Gorenstein et al.
Dhavalkumar Patel et al.
Background: Predicting hospitalization from nurse triage notes has the potential to augment care. However, there needs to be careful considerations for which models to choose for this goal. Specifically, health systems wi...
Exploring Machine Learning Applications in Pediatric Asthma Management: Scoping Review [0.03%]
儿科哮喘管理中机器学习应用探究：循证评议式回顾研究
Tanvi Ojha,Atushi Patel,Krishihan Sivapragasam et al.
Tanvi Ojha et al.
Background: The integration of machine learning (ML) in predicting asthma-related outcomes in children presents a novel approach in pediatric health care. ...
Review
JMIR AI. 2024 Aug 27:3:e57983. DOI:10.2196/57983 2024
Mitigating Sociodemographic Bias in Opioid Use Disorder Prediction: Fairness-Aware Machine Learning Framework [0.03%]
缓解阿片类药物使用障碍预测中的社会人口偏见：一种公平性感知的机器学习框架
Mohammad Yaseliani,Md Noor-E-Alam,Md Mahmudul Hasan
Mohammad Yaseliani
Background: Opioid use disorder (OUD) is a critical public health crisis in the United States, affecting >5.5 million Americans in 2021. Machine learning has been used to predict patient risk of incident OUD. However, lit...
Evaluating Literature Reviews Conducted by Humans Versus ChatGPT: Comparative Study [0.03%]
人机对比：文献综述评价之比较研究
Mehrnaz Mostafapour,Jacqueline H Fortier,Karen Pacheco et al.
Mehrnaz Mostafapour et al.
Background: With the rapid evolution of artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT-4 (OpenAI), there is an increasing interest in their potential to assist in scholarly tasks, ...