Harnessing computational tools of the digital era for enhanced infection control [0.03%]
利用数字时代的计算工具增强感染控制能力
Francesco Branda
Francesco Branda
This paper explores the potential of artificial intelligence, machine learning, and big data analytics in revolutionizing infection control. It addresses the challenges and innovative approaches in combating infectious diseases and antimicr...
Clinician voices on ethics of LLM integration in healthcare: a thematic analysis of ethical concerns and implications [0.03%]
临床医生关于LLM在医疗保健中伦理整合的观点：伦理关切和影响的主题分析
Tala Mirzaei,Leila Amini,Pouyan Esmaeilzadeh
Tala Mirzaei
Objectives: This study aimed to explain and categorize key ethical concerns about integrating large language models (LLMs) in healthcare, drawing particularly from the perspectives of clinicians in online discussions. ...
Analysis of anterior segment in primary angle closure suspect with deep learning models [0.03%]
基于深度学习模型的原发性闭角疑诊患者眼前节分析研究
Ziwei Fu,Jinwei Xi,Zhi Ji et al.
Ziwei Fu et al.
Objective: To analyze primary angle closure suspect (PACS) patients' anatomical characteristics of anterior chamber configuration, and to establish artificial intelligence (AI)-aided diagnostic system for PACS screening. ...
Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3 [0.03%]
基于机器学习的预测模型在Sepsis-3中用于30天死亡率预测
Md Sohanur Rahman,Khandaker Reajul Islam,Johayra Prithula et al.
Md Sohanur Rahman et al.
Background: Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques...
Data privacy-aware machine learning approach in pancreatic cancer diagnosis [0.03%]
兼顾数据隐私的机器学习胰腺癌诊断方法
Ömer Faruk Akmeşe
Ömer Faruk Akmeşe
Problem: Pancreatic ductal adenocarcinoma (PDAC) is considered a highly lethal cancer due to its advanced stage diagnosis. The five-year survival rate after diagnosis is less than 10%. However, if diagnosed early, the fiv...
Trustworthy and ethical AI-enabled cardiovascular care: a rapid review [0.03%]
值得信赖且具有道德的心血管护理AI：快速审查
Maryam Mooghali,Austin M Stroud,Dong Whi Yoo et al.
Maryam Mooghali et al.
Background: Artificial intelligence (AI) is increasingly used for prevention, diagnosis, monitoring, and treatment of cardiovascular diseases. Despite the potential for AI to improve care, ethical concerns and mistrust in...
lab2clean: a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses [0.03%]
新颖的算法用于回顾性临床实验室结果数据的自动化清理以供二次使用-Lab2Clean
Ahmed Medhat Zayed,Arne Janssens,Pavlos Mamouris et al.
Ahmed Medhat Zayed et al.
Background: The integrity of clinical research and machine learning models in healthcare heavily relies on the quality of underlying clinical laboratory data. However, the preprocessing of this data to ensure its reliabil...
Factors affecting the survival of prediabetic patients: comparison of Cox proportional hazards model and random survival forest method [0.03%]
影响糖尿病前期患者生存的因素：Cox比例风险模型与随机生存森林方法的比较
Mehdi Sharafi,Mohammad Ali Mohsenpour,Sima Afrashteh et al.
Mehdi Sharafi et al.
Background: The worldwide prevalence of type 2 diabetes mellitus in adults is experiencing a rapid increase. This study aimed to identify the factors affecting the survival of prediabetic patients using a comparison of th...
Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study [0.03%]
整合多组学数据中多种数据类型可改善或阻碍生存预测性能吗？来自大规模基准研究的见解
Yingxia Li,Tobias Herold,Ulrich Mansmann et al.
Yingxia Li et al.
Background: Predictive modeling based on multi-omics data, which incorporates several types of omics data for the same patients, has shown potential to outperform single-omics predictive modeling. Most research in this do...
Common data quality elements for health information systems: a systematic review [0.03%]
卫生信息系统常见数据质量要素：系统综述
Hossein Ghalavand,Saied Shirshahi,Alireza Rahimi et al.
Hossein Ghalavand et al.
Background: Data quality in health information systems has a complex structure and consists of several dimensions. This research conducted for identify Common data quality elements for health information systems. ...