Identifying episodes of care in hospital admissions data for measures of disease burden: A tutorial and protocol for individual-level data analysis [0.03%]
基于住院记录识别疾病负担测量中的医疗事件：个体数据分析教程和方案
Jedidiah I Morton,Adam Livori,Lee Nedkoff et al.
Jedidiah I Morton et al.
Background: We are not aware of any comprehensive, publicly available, standardised protocol or syntax for the processing of hospital admissions data for individual-level analysis. Failure to appropriately process and ana...
Accuracy of ICD-10-PCS codes to identify Cardiac Implantable electronic devices in Portugal - A single center chart review [0.03%]
ICD-10-PCS代码在葡萄牙识别心血管植入式电子设备的准确性-单中心图表审查
Sandra Couto,Mariana Lobo,Fernando Lopes et al.
Sandra Couto et al.
Background: Hospital use of Cardiac Implantable Electronic Devices (CIEDs) is documented in the NHMD (National Hospital Morbidity Database) using the International Classification of Diseases Procedure Coding System (ICD-1...
Comparing logistic regression and machine learning for obesity risk prediction: A systematic review and meta-analysis [0.03%]
比较逻辑回归和机器学习在肥胖风险预测中的应用：系统评价与荟萃分析
Nancy Fosua Boakye,Ciarán Courtney O&#x;Toole,Amirhossein Jalali et al.
Nancy Fosua Boakye et al.
Background: Logistic regression (LR) has traditionally been the standard method used for predicting binary health outcomes; however, machine learning (ML) methods are increasingly popular. ...
Ethical aspects and user preferences in applying machine learning to adjust eHealth addressing substance use: A mixed-methods study [0.03%]
在使用机器学习调整eHealth以应对物质滥用中的伦理方面和用户偏好：一项混合方法研究
Marloes E Derksen,Max van Beek,Tamara de Bruijn et al.
Marloes E Derksen et al.
Background: Digital health interventions targeting substance use disorders are being increasingly implemented. Data science methodology has the potential to enhance involvement and efficacy of these interventions, though ...
Development and validation of a machine learning prognostic model based on an epigenomic signature in patients with pancreatic ductal adenocarcinoma [0.03%]
基于胰腺导管腺癌患者表观基因组特征的机器学习预后模型的开发及验证
Gian Maria Zaccaria,Nicola Altini,Valentina Mongelli et al.
Gian Maria Zaccaria et al.
Background: In Pancreatic Ductal Adenocarcinoma (PDAC), current prognostic scores are unable to fully capture the biological heterogeneity of the disease. While some approaches investigating the role of multi-omics in PDA...
Dermacen analytica: A novel methodology integrating multi-modal large language models with machine learning in dermatology [0.03%]
Dermacen analytica：结合多模态大型语言模型与机器学习在皮肤科中的新型方法论
Dimitrios P Panagoulias,Evridiki Tsoureli-Nikita,Maria Virvou et al.
Dimitrios P Panagoulias et al.
Objective: To design, implement, evaluate, and quantify a novel and adaptable Artificial Intelligence-empowered methodology aimed at supporting a dermatologist's workflow in assessing and diagnosing skin conditions, lever...
Understanding potentially inappropriate medication: A focus group study with general practitioners [0.03%]
理解潜在的不恰当药物使用：针对全科医生的焦点小组研究
Daniela A Rodrigues,Maria Teresa Herdeiro,Ramona Mateos-Campos et al.
Daniela A Rodrigues et al.
Introduction: Polypharmacy and Potentially Inappropriate Medication (PIM) pose significant challenges for older adults, frequently leading to adverse health outcomes. While digital health tools offer promise solutions to ...
Associations of dietary patterns with serum 25(OH) vitamin D and serum anemia related biomarkers among expectant mothers: A machine learning based approach [0.03%]
饮食模式与孕妇血清25(OH)维生素D和贫血相关生物标志物的关系：基于机器学习的方法
Arpita Das,Chyi-Huey Bai,Jung-Su Chang et al.
Arpita Das et al.
Background: Machine learning algorithms (MLA) gained prominence in nutritional epidemiology for analyzing dietary associations and uncovering intricate patterns within data. We explored dietary patterns associated with se...
Comparison of machine learning and logistic regression models for predicting emergence delirium in elderly patients: A prospective study [0.03%]
机器学习和逻辑回归模型在预测老年患者术后意识紊乱中的对比分析：前瞻性研究
Yufan Lu,Ying Li,Shengqiang Chi et al.
Yufan Lu et al.
Objective: To compare the performance of machine learning and logistic regression algorithms in predicting emergence delirium (ED) in elderly patients. Me...
Development and validation of a nomogram to predict the risk of in-hospital MACE for emergence NSTE-ACS: A retrospective multicenter study based on the Chinese population [0.03%]
基于中国人群的急诊非ST段抬高型急性冠脉综合征住院主要不良心脏事件风险列线图预测模型的建立与验证：一项回顾性多中心研究
Qianhui Zhou,Rui He,Hong Li et al.
Qianhui Zhou et al.
Purpose: Our study aims to develop and validate an effective in-hospital major adverse cardiovascular events(MACE) prediction model for patients with emergency Non-ST elevation acute coronary syndrome(NSTE-ACS). ...