Asghar Ehteshami,Ahamad-Reza Raeisi,Maedeh Rashedi et al.
Asghar Ehteshami et al.
Introduction: Developing key benchmarking indicators (KBIs) for Hospital Information Systems (HIS) has the potential to enhance operational efficiency and effectiveness, resulting in the successful achievement of hospital...
Mortality predicting models for patients with infective endocarditis: a machine learning approach [0.03%]
机器学习在感染性心内膜炎患者预后预测模型中的应用研究
Yang Zi-Yang,Wang Qi,Xingyan Liu et al.
Yang Zi-Yang et al.
Background: Infective endocarditis (IE) is a fatal cardiovascular disease with varied clinical manifestations but rapid progression. A series of existing risk models helped identify IE patients with high risk, but the imp...
Observational Study
BMC medical informatics and decision making. 2025 Jul 1;25(1):229. DOI:10.1186/s12911-025-03025-4 2025
Risk factors and nomogram model for short-term postoperative complications in patients with hirschsprung disease [0.03%]
短肠造口术患者并发症的风险因素及预测模型
Aohua Song,Bobin Zhang,Wei Feng et al.
Aohua Song et al.
Objective: Short-term postoperative complications (SPCs) in patients with Hirschsprung disease (HD) are a topic of concern. The aims of this study were to identify the risk factors for different severities of SPCs based o...
A deep learning model for predicting systemic lupus erythematosus-associated epitopes [0.03%]
一种预测系统性红斑狼疮相关表位的深度学习模型
Jiale He,Zixia Liu,Xiaopo Tang
Jiale He
Background: The accurate prediction of epitopes associated with Systemic Lupus Erythematosus (SLE) plays a vital role in advancing our understanding of autoimmune pathogenesis and in designing effective immunotherapeutics...
Development of an ensemble prediction model for acute graft-versus-host disease in allogeneic transplantation based on machine learning [0.03%]
基于机器学习的异基因移植急性移植物抗宿主病列队预测模型构建
Lin Song,Xingwei Wu,Mengjia Xu et al.
Lin Song et al.
Background: Acute graft-versus-host disease (aGVHD) is a major post-transplantation complication and one of the most significant causes of non-relapse-related death. However, the massive and complex clinical data make aGV...
Development and validation of machine learning classifiers for predicting treatment-needed retinopathy of prematurity [0.03%]
早产儿视网膜病变治疗需求预测的机器学习分类器的研发和验证
Nasser Shoeibi,Majid Abrishami,Seyedeh Maryam Hosseini et al.
Nasser Shoeibi et al.
Background: This study aims to design and evaluate various supervised machine-learning models for identifying premature infants who require treatment based on demographic data and clinical findings from screening examinat...
Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES [0.03%]
基于LightGBM建模和SHAP特征解释的NHANES人群肝脂肪代谢评分与心力衰竭非线性关联分析
Ningyi Cheng,Yukun Chen,Lei Jin et al.
Ningyi Cheng et al.
Objective: Using 2005-2018 NHANES data, this study examined the association between the visceral fat metabolism score (METS-VF) and heart failure (HF) prevalence in U.S. adults, leveraging machine learning (LightGBM/XGBoo...
Multiclass skin lesion classification and localziation from dermoscopic images using a novel network-level fused deep architecture and explainable artificial intelligence [0.03%]
基于新型网络级融合深度架构和可解释人工智能的皮肤病变多分类及定位方法研究——利用皮肤断层图像实现皮肤病灶分类与定位的新方法研究
Mehak Arshad,Muhammad Attique Khan,Nouf Abdullah Almujally et al.
Mehak Arshad et al.
Background and objective: Early detection and classification of skin cancer are critical for improving patient outcomes. Dermoscopic image analysis using Computer-Aided Diagnostics (CAD) is a powerful tool to assist derma...
Expert-augmented machine learning for predicting extubation readiness in the pediatric intensive care unit [0.03%]
基于专家知识的机器学习在儿科重症监护病房拔管时机预测中的应用
Jean Digitale,Deborah Franzon,Jin Ge et al.
Jean Digitale et al.
Background: Determining extubation readiness in pediatric intensive care units (PICU) is challenging. We used expert-augmented machine learning (EAML), a method that combines machine learning with human expert knowledge, ...
A dynamic prediction model for predicting the time at which patients with MCI progress to AD based on time-dependent covariates [0.03%]
基于时间依赖性协变量的MCI患者进展为AD的时间动态预测模型
Yanjie Wang,Yu Song,Chengfeng Zhang et al.
Yanjie Wang et al.
Background: Alzheimer's Disease (AD) is an irreversible neurodegenerative disorder that imposes a significant burden on families and society. Timely intervention during the transitional stages from Mild Cognitive Impairme...