Development of machine learning models to predict perioperative blood transfusion in hip surgery [0.03%]
髋关节手术围术期输血预测的机器学习模型研究
Han Zang,Ai Hu,Xuanqi Xu et al.
Han Zang et al.
Background: Allogeneic Blood transfusion is common in hip surgery but is associated with increased morbidity. Accurate prediction of transfusion risk is necessary for minimizing blood product waste and preoperative decisi...
Development of a web-based patient decision aid for myopia laser correction method [0.03%]
近视激光治疗决策支持工具网站版的开发
Hanieh Delshad Aghdam,Fatemeh Zarei,Seyed Farzad Mohammadi
Hanieh Delshad Aghdam
Background: In the context of healthcare centered on the patient, Patient Decision Aids (PtDAs) acts as an essential instrument, promoting shared decision-making (SDM). Considering the prevalent occurrence of myopia, the ...
Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records [0.03%]
利用电子病历进行自然语言处理以研究领域标准识别，从而提升创伤后应激障碍患者的评估水平
Oshin Miranda,Sophie Marie Kiehl,Xiguang Qi et al.
Oshin Miranda et al.
Background: Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The in...
DREAMER: a computational framework to evaluate readiness of datasets for machine learning [0.03%]
用于评估数据集机器学习准备情况的计算框架Dreamer
Meysam Ahangaran,Hanzhi Zhu,Ruihui Li et al.
Meysam Ahangaran et al.
Background: Machine learning (ML) has emerged as the predominant computational paradigm for analyzing large-scale datasets across diverse domains. The assessment of dataset quality stands as a pivotal precursor to the suc...
Extracting patient lifestyle characteristics from Dutch clinical text with BERT models [0.03%]
使用BERT模型从荷兰临床文本中提取患者生活方式特征
Hielke Muizelaar,Marcel Haas,Koert van Dortmont et al.
Hielke Muizelaar et al.
Background: BERT models have seen widespread use on unstructured text within the clinical domain. However, little to no research has been conducted into classifying unstructured clinical notes on the basis of patient life...
Tserendorj Chinbat,Samaneh Madanian,David Airehrour et al.
Tserendorj Chinbat et al.
Background: The increased application of Internet of Things (IoT) in healthcare, has fueled concerns regarding the security and privacy of patient data. Lightweight Cryptography (LWC) algorithms can be seen as a potential...
Correction: Deep learning model for differentiating nasal cavity masses based on nasal endoscopy images [0.03%]
订正：基于鼻内镜图像的鼻腔肿物深度学习模型区分能力研究
Junhu Tai,Munsoo Han,Bo Yoon Choi et al.
Junhu Tai et al.
A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient's medical history [0.03%]
基于深度学习模型的计算临床决策支持系统根据患者病史为儿童癫痫患者推荐有效的抗癫痫药物
Daeahn Cho,Myeong-Sang Yu,Jeongyoon Shin et al.
Daeahn Cho et al.
Background: Epilepsy, a chronic brain disorder characterized by abnormal brain activity that causes seizures and other symptoms, is typically treated using anti-epileptic drugs (AEDs) as the first-line therapy. However, d...
Interpretable machine learning model for predicting acute kidney injury in critically ill patients [0.03%]
重症患者急性肾损伤的可解释机器学习预测模型
Xunliang Li,Peng Wang,Yuke Zhu et al.
Xunliang Li et al.
Background: This study aimed to create a method for promptly predicting acute kidney injury (AKI) in intensive care patients by applying interpretable, explainable artificial intelligence techniques. ...
Exploring the tradeoff between data privacy and utility with a clinical data analysis use case [0.03%]
利用临床数据分析用例探索数据隐私和数据效用之间的权衡关系
Eunyoung Im,Hyeoneui Kim,Hyungbok Lee et al.
Eunyoung Im et al.
Background: Securing adequate data privacy is critical for the productive utilization of data. De-identification, involving masking or replacing specific values in a dataset, could damage the dataset's utility. However, f...