A systematic comparison of short-term and long-term mortality prediction in acute myocardial infarction using machine learning models [0.03%]
急性心肌梗死短期和长期死亡率的机器学习预测模型系统性比较研究
Yawei Yang,Junjie Tang,Liping Ma et al.
Yawei Yang et al.
Background and objective: The machine learning (ML) models for acute myocardial infarction (AMI) are considered to have better predictive ability for mortality compared to conventional risk scoring models. However, previo...
Observational Study
BMC medical informatics and decision making. 2025 Jun 5;25(1):208. DOI:10.1186/s12911-025-03052-1 2025
Advancing blood cell detection and classification: performance evaluation of modern deep learning models [0.03%]
推进血液细胞检测和分类：现代深度学习模型的性能评估
Shilpa Choudhary,Sandeep Kumar,Pammi Sri Siddhaarth et al.
Shilpa Choudhary et al.
The detection and classification of blood cells are important in diagnosing and monitoring a variety of blood-related illnesses, such as anemia, leukemia, and infection, all of which may cause significant mortality. Accurate blood cell iden...
Deep learning model applied to real-time delineation of colorectal polyps [0.03%]
深度学习模型在实时结直肠息肉分类中的应用研究
Moana Gelu-Simeon,Adel Mamou,Georgette Saint-Georges et al.
Moana Gelu-Simeon et al.
Background: Deep learning models have shown considerable potential to improve diagnostic accuracy across medical fields. Although YOLACT has demonstrated real-time detection and segmentation in non-medical datasets, its a...
Visualizing fatigue mechanisms in non-communicable diseases: an integrative approach with multi-omics and machine learning [0.03%]
非传染病疲劳机制的可视化：一种多组学和机器学习的综合方法
Yusuke Kobayashi,Naoki Fujiwara,Yuki Murakami et al.
Yusuke Kobayashi et al.
Background: Fatigue is a prevalent and debilitating symptom of non-communicable diseases (NCDs); however, its biological basis are not well-defined. This exploratory study aimed to identify key biological drivers of fatig...
Uncovering nonlinear patterns in time-sensitive prehospital breathing emergencies: an exploratory machine learning study [0.03%]
基于时间的院前呼吸急症中的非线性模式挖掘：一项探索性机器学习研究
Peter Hill,Daniel Jonsson,Jakob Lederman et al.
Peter Hill et al.
Background: Timely prehospital care is crucial for patients presenting with high-risk time-sensitive (HRTS) conditions. However, the interplay between response time and demographic factors in patients with breathing probl...
Observational Study
BMC medical informatics and decision making. 2025 Jun 3;25(1):205. DOI:10.1186/s12911-025-03046-z 2025
General practitioners' perceptions on decision aids in healthcare: a qualitative study in Portugal [0.03%]
葡萄牙全科医生对医疗决策辅助手段的看法：一项定性研究
Mafalda Proença-Portugal,Bruno Heleno,Sónia Dias et al.
Mafalda Proença-Portugal et al.
Background: Decision aids (DA) are evidence-based tools that support health-related decisions. Despite their recognised value, the use of DAs in primary care remains modest. In Portugal, clinical guidelines focus on clini...
Using self-generated identification codes to match anonymous longitudinal data in a sexual health study of secondary school students: a cohort study [0.03%]
中学学生性健康研究中使用自我生成识别码匹配匿名纵向数据的一项队列研究
Edmond Pui Hang Choi,Ellie Bostwick Andres,Heidi Sze Lok Fan et al.
Edmond Pui Hang Choi et al.
Objective: This study aimed to (i) describe the procedures for generating self-generated identification codes (SGICs) in a prospective longitudinal evaluation of a sexual health program for secondary school students in Ho...
AI-Powered early warning systems for clinical deterioration significantly improve patient outcomes: a meta-analysis [0.03%]
人工智能驱动的临床恶化早期预警系统可显著改善患者预后：一项荟萃分析研究
Shixin Yuan,Zihuan Yang,Junjie Li et al.
Shixin Yuan et al.
Background: Clinical deterioration is often preceded by subtle physiological changes that, if unheeded, can lead to adverse patient outcomes. The precision of traditional scoring systems in detecting these precursors has ...
Deep learning-driven modality imputation and subregion segmentation to enhance high-grade glioma grading [0.03%]
基于深度学习的模态插补和次区域分割以增强高度胶质瘤分级
Jiabin Yu,Qi Liu,Chenjie Xu et al.
Jiabin Yu et al.
Purpose: This study aims to develop a deep learning framework that leverages modality imputation and subregion segmentation to improve grading accuracy in high-grade gliomas. ...
Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit [0.03%]
重症监护病房急性胰腺炎住院死亡率的机器学习预测模型
Shuxing Wei,Hongmeng Dong,Weidong Yao et al.
Shuxing Wei et al.
Background: Acute pancreatitis (AP) represents a critical medical condition where timely and precise prediction of in-hospital mortality is crucial for guiding optimal clinical management. This study focuses on the develo...