Predicting angiographic coronary artery disease using machine learning and high-frequency QRS [0.03%]
基于机器学习和高频QRS预测冠状动脉造影疾病
Jiajia Zhang,Heng Zhang,Ting Wei et al.
Jiajia Zhang et al.
Aim: Exercise stress ECG is a common diagnostic test for stable coronary artery disease, but its sensitivity and specificity need to be further improved. In this paper, we construct a machine learning model for the predic...
A machine learning approach to determine the risk factors for fall in multiple sclerosis [0.03%]
基于机器学习的多发性硬化症坠落风险因素分析方法研究
Su Özgür,Meryem Koçaslan Toran,İsmail Toygar et al.
Su Özgür et al.
Background: Falls in multiple sclerosis can result in numerous problems, including injuries and functional loss. Therefore, determining the factors contributing to falls in people with Multiple Sclerosis (PwMS) is crucial...
Can artificial intelligence models serve as patient information consultants in orthodontics? [0.03%]
人工智能模型能否在正畸学中担任患者信息顾问？
Derya Dursun,Rumeysa Bilici Geçer
Derya Dursun
Background: To evaluate the accuracy, reliability, quality, and readability of responses generated by ChatGPT-3.5, ChatGPT-4, Gemini, and Copilot in relation to orthodontic clear aligners. ...
The Ugandan sickle Pan-African research consortium registry: design, development, and lessons [0.03%]
乌干达镰状_pan-非洲研究联盟注册表：设计、开发和经验教训
Mike Nsubuga,Henry Mutegeki,Daudi Jjingo et al.
Mike Nsubuga et al.
Background: Sub-Saharan Africa bears the highest burden of sickle cell disease (SCD) globally with Nigeria, Democratic Republic of Congo, Tanzania, Uganda being the most affected countries. Uganda reports approximately 20...
Creating a health informatics data resource for hearing health research [0.03%]
用于听力健康研究的卫生信息学数据资源的建立
Nishchay Mehta,Baptiste Briot Ribeyre,Lilia Dimitrov et al.
Nishchay Mehta et al.
Background: The National Institute of Health and Social Care Research (NIHR) Health Informatics Collaborative (HIC) for Hearing Health has been established in the UK to curate routinely collected hearing health data to ad...
Long-term prediction of Iranian blood product supply using LSTM: a 5-year forecast [0.03%]
基于LSTM的伊朗血液制品长期供应预测——五年期展望
Ebrahim Miri-Moghaddam,Saeede Khosravi Bizhaem,Zohre Moezzifar et al.
Ebrahim Miri-Moghaddam et al.
Background: This study aims to predict the trend of procurement and storage of various blood products, as well as planning and monitoring the consumption of blood products in different centers across Iran based on artific...
Michelle Pistner Nixon,Farhani Momotaz,Claire Smith et al.
Michelle Pistner Nixon et al.
Background: A central goal of modern evidence-based medicine is the development of simple and easy to use tools that help clinicians integrate quantitative information into medical decision-making. The Bayesian Pre-test/P...
Sumit Madan,Manuel Lentzen,Johannes Brandt et al.
Sumit Madan et al.
Deep neural networks (DNN) have fundamentally revolutionized the artificial intelligence (AI) field. The transformer model is a type of DNN that was originally used for the natural language processing tasks and has since gained more and mor...
Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury [0.03%]
机器学习预测脓毒症相关急性肾损伤患者一年死亡率
Le Li,Jingyuan Guan,Xi Peng et al.
Le Li et al.
Introduction: Sepsis-associated acute kidney injury (SA-AKI) is strongly associated with poor prognosis. We aimed to build a machine learning (ML)-based clinical model to predict 1-year mortality in patients with SA-AKI. ...
The sensitivity outcome index system for home care of elderly liver transplant patients was developed based on the Omaha problem classification system [0.03%]
基于奥马哈问题分类系统的老年肝移植患者居家护理敏感结局指标体系的构建
Bin Wang,Xia Huang,Guofang Liu et al.
Bin Wang et al.
Objective: Based on the Omaha problem classification system, a sensitivity outcome index system for home nursing of elderly liver transplant patients was established. ...