Effect of early serum phosphate disorder on in-hospital and 28-day mortality in sepsis patients: a retrospective study based on MIMIC-IV database [0.03%]
脓毒症患者早期血清磷酸盐紊乱对住院和28天死亡率的影响：基于MIMIC-IV数据库的回顾性研究
Yinghao Luo,Yahui Peng,Yujia Tang et al.
Yinghao Luo et al.
Background: This study aims to assess the influence of early serum phosphate fluctuation on the short-term prognosis of sepsis patients. Methods: ...
Elias Chaibub Neto,Vijay Yadav,Solveig K Sieberts et al.
Elias Chaibub Neto et al.
Background: The two-way partial AUC has been recently proposed as a way to directly quantify partial area under the ROC curve with simultaneous restrictions on the sensitivity and specificity ranges of diagnostic tests or...
Exploring the potential of ChatGPT as an adjunct for generating diagnosis based on chief complaint and cone beam CT radiologic findings [0.03%]
探讨ChatGPT作为辅助工具根据主诉和锥形束CT影像学发现生成诊断的潜力
Yanni Hu,Ziyang Hu,Wenjing Liu et al.
Yanni Hu et al.
Aim: This study aimed to assess the performance of OpenAI's ChatGPT in generating diagnosis based on chief complaint and cone beam computed tomography (CBCT) radiologic findings. ...
Correction: Susceptibility of AutoML mortality prediction algorithms to model drift caused by the COVID pandemic [0.03%]
修正：自动机器学习死亡率预测算法对由COVID大流行引起的模型漂移的敏感性
Simone Maria Kagerbauer,Bernhard Ulm,Armin Horst Podtschaske et al.
Simone Maria Kagerbauer et al.
Automatic de-identification of French electronic health records: a cost-effective approach exploiting distant supervision and deep learning models [0.03%]
法语电子健康记录的自动去识别：利用远程监督和深度学习模型的成本效益方法
Mohamed El Azzouzi,Gouenou Coatrieux,Reda Bellafqira et al.
Mohamed El Azzouzi et al.
Background: Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is ther...
InsightSleepNet: the interpretable and uncertainty-aware deep learning network for sleep staging using continuous Photoplethysmography [0.03%]
InsightSleepNet：用于连续光电容积脉搏波描记法睡眠分期的可解释且具有不确定意识的深度学习网络
Borum Nam,Beomjun Bark,Jeyeon Lee et al.
Borum Nam et al.
Background: This study was conducted to address the existing drawbacks of inconvenience and high costs associated with sleep monitoring. In this research, we performed sleep staging using continuous photoplethysmography (...
Which risk factor best predicts coronary artery disease using artificial neural network method? [0.03%]
人工神经网络方法预测冠状动脉疾病的最佳危险因素是什么？
Nahid Azdaki,Fatemeh Salmani,Toba Kazemi et al.
Nahid Azdaki et al.
Background: Coronary artery disease (CAD) is recognized as the leading cause of death worldwide. This study analyses CAD risk factors using an artificial neural network (ANN) to predict CAD. ...
Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data [0.03%]
改进心力衰竭治疗决策：使用电子健康记录数据的可解释机器学习模型进行高级疗法资格预测
Yufeng Zhang,Jessica R Golbus,Emily Wittrup et al.
Yufeng Zhang et al.
Timely and accurate referral of end-stage heart failure patients for advanced therapies, including heart transplants and mechanical circulatory support, plays an important role in improving patient outcomes and saving costs. However, the de...
Dimension reduction and outlier detection of 3-D shapes derived from multi-organ CT images [0.03%]
多器官CT图像中三维形状的降维和离群点检测
Michael Selle,Magdalena Kircher,Cornelia Schwennen et al.
Michael Selle et al.
Background: Unsupervised clustering and outlier detection are important in medical research to understand the distributional composition of a collective of patients. A number of clustering methods exist, also for high-dim...
Characterizing the limitations of using diagnosis codes in the context of machine learning for healthcare [0.03%]
在医疗保健机器学习背景下使用诊断代码的局限性分析
Lin Lawrence Guo,Keith E Morse,Catherine Aftandilian et al.
Lin Lawrence Guo et al.
Background: Diagnostic codes are commonly used as inputs for clinical prediction models, to create labels for prediction tasks, and to identify cohorts for multicenter network studies. However, the coverage rates of diagn...