Sepsis Prediction at Emergency Department Triage Using Natural Language Processing: Retrospective Cohort Study [0.03%]
基于自然语言处理的急诊分诊中使用机器学习预测脓毒症：回顾性队列研究
Felix Brann,Nicholas William Sterling,Stephanie O Frisch et al.
Felix Brann et al.
Background: Despite its high lethality, sepsis can be difficult to detect on initial presentation to the emergency department (ED). Machine learning-based tools may provide avenues for earlier detection and lifesaving int...
Sample Size Considerations for Fine-Tuning Large Language Models for Named Entity Recognition Tasks: Methodological Study [0.03%]
大型语言模型微调以进行命名实体识别任务的样本量考量：方法学研究
Zoltan P Majdik,S Scott Graham,Jade C Shiva Edward et al.
Zoltan P Majdik et al.
Background: Large language models (LLMs) have the potential to support promising new applications in health informatics. However, practical data on sample size considerations for fine-tuning LLMs to perform specific tasks...
Nicola Luigi Bragazzi,Sergio Garbarino
Nicola Luigi Bragazzi
Clinical decision-making is a crucial aspect of health care, involving the balanced integration of scientific evidence, clinical judgment, ethical considerations, and patient involvement. This process is dynamic and multifaceted, relying on...
Risk Perception, Acceptance, and Trust of Using AI in Gastroenterology Practice in the Asia-Pacific Region: Web-Based Survey Study [0.03%]
亚太地区胃肠病学实践中的人工智能风险感知、接受度和信任的网络调查研究
Wilson Wb Goh,Kendrick Ya Chia,Max Fk Cheung et al.
Wilson Wb Goh et al.
Background: The use of artificial intelligence (AI) can revolutionize health care, but this raises risk concerns. It is therefore crucial to understand how clinicians trust and accept AI technology. Gastroenterology, by i...
Predictive Performance of Machine Learning-Based Models for Poststroke Clinical Outcomes in Comparison With Conventional Prognostic Scores: Multicenter, Hospital-Based Observational Study [0.03%]
基于机器学习的模型与传统预后评分在预测卒中后临床结局方面的预测性能比较：多中心医院基于观察的研究所见
Fumi Irie,Koutarou Matsumoto,Ryu Matsuo et al.
Fumi Irie et al.
Background: Although machine learning is a promising tool for making prognoses, the performance of machine learning in predicting outcomes after stroke remains to be examined. ...
Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review [0.03%]
改善人工智能在卫生公平性方面影响的策略：循证回顾
Carl Thomas Berdahl,Lawrence Baker,Sean Mann et al.
Carl Thomas Berdahl et al.
Background: Emerging artificial intelligence (AI) applications have the potential to improve health, but they may also perpetuate or exacerbate inequities. ...
Review
JMIR AI. 2023 Feb 7:2:e42936. DOI:10.2196/42936 2023
Machine Learning-Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review [0.03%]
基于常规收集的电子健康记录的机器学习哮喘发作预测模型：系统回顾
Arif Budiarto,Kevin C H Tsang,Andrew M Wilson et al.
Arif Budiarto et al.
Background: An early warning tool to predict attacks could enhance asthma management and reduce the likelihood of serious consequences. Electronic health records (EHRs) providing access to historical data about patients w...
Review
JMIR AI. 2023 Dec 7:2:e46717. DOI:10.2196/46717 2023
Role of Ethics in Developing AI-Based Applications in Medicine: Insights From Expert Interviews and Discussion of Implications [0.03%]
医学中基于人工智能的应用程序开发中的伦理作用：专家访谈见解及影响讨论
Lukas Weidener,Michael Fischer
Lukas Weidener
Background: The integration of artificial intelligence (AI)-based applications in the medical field has increased significantly, offering potential improvements in patient care and diagnostics. However, alongside these ad...
Learning From International Comparators of National Medical Imaging Initiatives for AI Development: Multiphase Qualitative Study [0.03%]
国际医学影像计划比较研究助力AI发展：多阶段定性分析研究
Kassandra Karpathakis,Emma Pencheon,Dominic Cushnan
Kassandra Karpathakis
Background: The COVID-19 pandemic drove investment and research into medical imaging platforms to provide data to create artificial intelligence (AI) algorithms for the management of patients with COVID-19. Building on th...
An Environmental Uncertainty Perception Framework for Misinformation Detection and Spread Prediction in the COVID-19 Pandemic: Artificial Intelligence Approach [0.03%]
基于人工智能的新冠疫情谣言检测与传播预测的环境不确定性感知框架
Jiahui Lu,Huibin Zhang,Yi Xiao et al.
Jiahui Lu et al.
Background: Amidst the COVID-19 pandemic, misinformation on social media has posed significant threats to public health. Detecting and predicting the spread of misinformation are crucial for mitigating its adverse effects...