Development and application of an early prediction model for risk of bloodstream infection based on real-world study [0.03%]
基于真实世界研究的血流感染风险早期预测模型的构建及应用
Xiefei Hu,Shenshen Zhi,Yang Li et al.
Xiefei Hu et al.
Background: Bloodstream Infection (BSI) is a severe systemic infectious disease that can lead to sepsis and Multiple Organ Dysfunction Syndrome (MODS), resulting in high mortality rates and posing a major public health bu...
A knowledge-based clinical decision support system for personalized health examination items in China: design and evaluation [0.03%]
基于知识的中国个性化健康体检项目智能决策支持系统设计与评估
Dan Wu,Jiye An,Shan Nan et al.
Dan Wu et al.
Background: Health examination identifies risk factors and diseases at an early stage through a series of health examination items. In China, however, the incidence of consulting services for health examination items is l...
Classification of lung cancer severity using gene expression data based on deep learning [0.03%]
基于深度学习的基因表达数据的肺癌严重程度分类
Ali Bou Nassif,Nour Ayman Abujabal,Aya Alchikh Omar
Ali Bou Nassif
Lung cancer is one of the most prevalent diseases affecting people and is a main factor in the rising death rate. Recently, Machine Learning (ML) and Deep Learning (DL) techniques have been utilized to detect and classify various types of c...
Application of artificial intelligence medical imaging aided diagnosis system in the diagnosis of pulmonary nodules [0.03%]
人工智能医学影像辅助诊断系统在肺结节诊断中应用研究
Ya Yang,Pan Wang,Chengzhou Yu et al.
Ya Yang et al.
The application of artificial intelligence (AI) technology has realized the transformation of people's production and lifestyle, and also promoted the rapid development of the medical field. At present, the application of intelligence in th...
Optimizing unsupervised feature engineering and classification pipelines for differentiated thyroid cancer recurrence prediction [0.03%]
优化未监督特征工程和分类管道以预测分化型甲状腺癌复发
Emmanuel Onah,Uche Jude Eze,Abdullahi Salahudeen Abdulraheem et al.
Emmanuel Onah et al.
Background: Differentiated thyroid cancer (DTC) is a common endocrine malignancy with rising incidence and frequent recurrence, despite a generally favorable prognosis. Accurate recurrence prediction is critical for guidi...
Insights into healthcare workers' perceptions of electronic medical record system utilization: a cross-sectional study in Mafeteng district, Lesotho [0.03%]
莱索托马费腾地区医护人员对电子病历系统利用的看法：横断面研究
Tebeli E Sekoai,Astrid Turner,Janine Mitchell
Tebeli E Sekoai
Background: Electronic medical record (EMR) systems have significantly transformed how healthcare data is created, managed, and utilized, offering improved legibility, accessibility, and support for clinical decision-maki...
Auto-expansion software prompting reduces abbreviation use in electronic hospital discharge letters: an observational pre- and post-intervention study [0.03%]
自动扩展软件提示可减少电子出院信中的缩写使用：一项前瞻性观察研究
Shamus Toomath,Emily J Hibbert
Shamus Toomath
Background: Abbreviation use remains a significant cause of miscommunication among healthcare practitioners worldwide, creating uncertainty in interpretation and leading to poorer patient outcomes. This study aimed to ass...
Observational Study
BMC medical informatics and decision making. 2025 May 1;25(1):180. DOI:10.1186/s12911-025-03005-8 2025
A hybrid approach for binary and multi-class classification of voice disorders using a pre-trained model and ensemble classifiers [0.03%]
基于预训练模型和集成分类器的语音障碍二元及多元分类的混合方法研究
Mehtab Ur Rahman,Cem Direkoglu
Mehtab Ur Rahman
Recent advances in artificial intelligence-based audio and speech processing have increasingly focused on the binary and multi-class classification of voice disorders. Despite progress, achieving high accuracy in multi-class classification ...
Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome [0.03%]
利用SEQENS识别相关特征以改进预测AML治疗效果的监督机器学习模型
Pedro Pons-Suñer,François Signol,Noemi Alvarez et al.
Pedro Pons-Suñer et al.
Background and objective: This study has two main objectives. First, to evaluate a feature selection methodology based on SEQENS, an algorithm for identifying relevant variables. Second, to validate machine learning model...
Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model [0.03%]
基于临床和实验室数据的机器学习模型在早期预测子痫前期中的应用研究
Songchang Chen,Jia Li,Xiao Zhang et al.
Songchang Chen et al.
Background: This study was performed to characterize the relationship of various laboratory test indicators with clinical information and Preeclampsia (PE) development. Then, prediction models for early-onset preeclampsia...