Combining deep learning and machine learning for the automatic identification of hip prosthesis failure: Development, validation and explainability analysis [0.03%]
基于深度学习和机器学习的髋关节假体失效自动识别：开发、验证与可解释性分析
Federico Muscato,Anna Corti,Francesco Manlio Gambaro et al.
Federico Muscato et al.
Aim: Revision hip arthroplasty has a less favorable outcome than primary total hip arthroplasty and an understanding of the timing of total hip arthroplasty failure may be helpful. The aim of this study is to develop a co...
Comparison of the validity, perceived usefulness, and usability of I-MeDeSA and TEMAS, two tools to evaluate alert system usability [0.03%]
I-MeDeSA和TEMAS两种评估报警系统可用性工具的有效性、实用性及易用性的比较
Romaric Marcilly,Wu-Yi Zheng,Paul Quindroit et al.
Romaric Marcilly et al.
Objective: Two tools are currently available in the literature to evaluate the usability of medication alert systems, the instrument for evaluating human factors principles in medication-related decision support alerts (I...
Evaluation of stacked ensemble model performance to predict clinical outcomes: A COVID-19 study [0.03%]
评估堆叠集成模型性能以预测临床结局：一项关于COVID-19的研究
Rianne Kablan,Hunter A Miller,Sally Suliman et al.
Rianne Kablan et al.
Background: The application of machine learning (ML) to analyze clinical data with the goal to predict patient outcomes has garnered increasing attention. Ensemble learning has been used in conjunction with ML to improve ...
Usability of a mobile application for the clinical follow-up of patients with chronic obstructive pulmonary disease and home oxygen therapy [0.03%]
慢性阻塞性肺病及家庭氧疗患者的移动应用临床随访实用性研究
Anisbed Naranjo-Rojas,Luis Ángel Perula-de Torres,Freiser Eceomo Cruz-Mosquera et al.
Anisbed Naranjo-Rojas et al.
Background: Technological health tools (e-Health) may potentially facilitate the treatment of patients with chronic diseases through development of self-management and -care skills in patients and caregivers. However, the...
Can deep learning on retinal images augment known risk factors for cardiovascular disease prediction in diabetes? A prospective cohort study from the national screening programme in Scotland [0.03%]
基于视网膜图像的深度学习能否增强已知的心血管疾病风险因素以改善糖尿病患者的心血管疾病预测？来自苏格兰国家筛查计划的一项前瞻性队列研究
Joseph Mellor,Wenhua Jiang,Alan Fleming et al.
Joseph Mellor et al.
Aims: This study's objective was to evaluate whether deep learning (DL) on retinal photographs from a diabetic retinopathy screening programme improve prediction of incident cardiovascular disease (CVD). ...
Evaluation of mobile applications focused on the care of patients with anxiety disorders: A systematic review in app stores in Brazil [0.03%]
针对焦虑障碍患者的移动应用的系统评价：巴西应用商店的 APP 评估
Viviane Souza do Nascimento,Aline Teotonio Rodrigues,Inajara Rotta et al.
Viviane Souza do Nascimento et al.
Objective: To identify and evaluate the quality of mobile apps available in Brazil focused on the care of patients with anxiety disorders. Methods: ...
Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review [0.03%]
基于可解释人工智能和机器学习技术的疾病共病预测系统评价研究
Mohanad M Alsaleh,Freya Allery,Jung Won Choi et al.
Mohanad M Alsaleh et al.
Objective: Disease comorbidity is a major challenge in healthcare affecting the patient's quality of life and costs. AI-based prediction of comorbidities can overcome this issue by improving precision medicine and providi...
Evaluation of machine learning-based models for prediction of clinical deterioration: A systematic literature review [0.03%]
基于机器学习的临床病情恶化预测模型的系统文献评价研究
Sepideh Jahandideh,Guncag Ozavci,Berhe W Sahle et al.
Sepideh Jahandideh et al.
Background and objective: Early identification of patients at risk of deterioration can prevent life-threatening adverse events and shorten length of stay. Although there are numerous models applied to predict patient cli...
Generalizable calibrated machine learning models for real-time atrial fibrillation risk prediction in ICU patients [0.03%]
重症监护室患者实时房颤风险预测的泛化校准机器学习模型
Jarne Verhaeghe,Thomas De Corte,Christopher M Sauer et al.
Jarne Verhaeghe et al.
Background: Atrial Fibrillation (AF) is the most common arrhythmia in the intensive care unit (ICU) and is associated with increased morbidity and mortality. Identification of patients at risk for AF is not routinely perf...
Clinicians' perspectives on wearable sensor technology as an alternative bedside monitoring tool in two West African countries [0.03%]
西非两国临床医生对可穿戴传感器技术作为床边监测替代工具的看法
Hassan M Ghomrawi,Benjamin T Many,Jane L Holl et al.
Hassan M Ghomrawi et al.
Objective: Healthcare facilities in low- and middle-income countries (LMICs), especially in Africa, suffer from a lack of continuous bedside monitoring capability, adversely affecting timely detection of hemodynamic deter...