Prediction cardiovascular deterioration in a paediatric intensive care unit (PicEWS): a machine learning modelling study of routinely collected health-care data [0.03%]
基于常规收集的医疗数据预测儿科重症监护病房的心血管功能恶化（PicEWS）：一项机器学习建模研究
Dan Fredman Stein,Michael J Carter,John Booth et al.
Dan Fredman Stein et al.
Background: Paediatric intensive care medicine uses fine granular clinical data that describe substantial patient instability to make high-consequence decisions. However, these decisions are also hindered by clinical expe...
Predicting task performance in robot-assisted surgery using physiological stress and subjective workload: a case study with interpretable machine learning [0.03%]
基于生理压力和主观工作负荷预测机器人辅助手术中的任务表现：具有可解释机器学习的案例研究
Kaiqi Wei,Chika Kimura,Megumi Shimura et al.
Kaiqi Wei et al.
Robot-assisted surgery (RAS) enhances surgical precision and extends surgeons' capabilities. However, its effects on the cognitive and physical states of surgeons remain poorly understood. It is essential to investigate the workload and phy...
Interpretable machine learning model for identification and risk factor of premature rupture of membranes (PROM) and its association with nutritional inflammatory index: a retrospective study [0.03%]
可解释的机器学习模型在胎膜早破（PROM）识别及危险因素分析中的应用及其与营养炎症指数的关系：一项回顾性研究
Meng Zheng,Xiaowei Zhang,Haihong Wang et al.
Meng Zheng et al.
Background: Premature rupture of membranes (PROM) poses significant risks to both maternal and neonatal health. This study aims to construct a risk factor prediction model related to PROM by using machine learning technol...
Development and validation of an interpretable machine learning model for standard spleen volume prediction [0.03%]
一种可解释的机器学习模型的发展和验证，用于标准脾体积预测
Jinyu Lin,Jian Yang,Yinling Qian et al.
Jinyu Lin et al.
Background: Splenomegaly serves as a crucial indicator for various diseases, particularly in hepatosplenomegaly and hematological disorders. Accurate assessment of splenomegaly is essential for improving diagnostic accura...
A three-classification machine learning model for non-invasive prediction of molecular subtypes in diffuse glioma: a two-center study [0.03%]
一种用于弥漫性胶质瘤分子亚型非侵入预测的机器学习分类模型：一项多中心研究
Meilin Zhu,Weishu Hou,Jiahao Gao et al.
Meilin Zhu et al.
Background: Determining the molecular status of gliomas is crucial for evaluating treatment efficacy and prognosis. However, this process currently requires the invasive and cumbersome method of histological analysis. We ...
Predicting malignant cerebral edema after acute ischemic stroke: a machine-learning model with multi-region radiomics [0.03%]
基于机器学习的急性缺血性脑卒中恶性脑水肿多区域影像组学预测模型研究
Lingfeng Zhang,Yue Zhang,Chunyan Yang et al.
Lingfeng Zhang et al.
Background: Malignant cerebral edema (MCE) is a severe complication of acute ischemic stroke (AIS) that is associated with poor outcomes or death. The study sought to develop a predictive machine learning (ML)-based model...
Development and validation of an MRI radiomics-based interpretable machine learning model for predicting the progression-free survival in locally advanced nasopharyngeal carcinoma [0.03%]
基于MRI影像组学的机器学习模型预测局部晚期鼻咽癌无进展生存期的研究及验证
Penghao Lai,Xiaobo Chen,Wei Pei et al.
Penghao Lai et al.
Background: Locally advanced nasopharyngeal carcinoma (LANPC) is a common malignant tumor of the nasopharynx, characterized by poor prognosis and a high susceptibility to recurrence and metastasis after surgery. The aim o...
Integrative machine learning reveals the biological function and prognostic significance of α-ketoglutarate in gastric cancer [0.03%]
集成机器学习揭示α-酮戊二酸在胃癌中的生物功能和预后意义
Fangyuan Liu,Xuemeng Sun,Yun Zeng et al.
Fangyuan Liu et al.
Gastric cancer (GC) has a poor response to treatment, an unfavorable prognosis and a lack of reliable biomarkers for predicting disease progression and therapeutic outcomes. α-Ketoglutarate (α-KG) is a critical metabolite involved in cell...
Mohammed Almulhim,Dunya Alfaraj,Dina Alabbad et al.
Mohammed Almulhim et al.
Background: Triage is a critical component of Emergency department care. Erroneous patient classification and mis-triaging are common in present triage systems worldwide. Therefore, several institutes worldwide have devel...
Construction of a risk prediction model for pulmonary infection in patients with spontaneous intracerebral hemorrhage during the recovery phase based on machine learning [0.03%]
基于机器学习的自发性脑出血恢复期患者肺部感染风险预测模型构建
Jixiang Xu,Yuan Li,Fumin Zhu et al.
Jixiang Xu et al.
Objective: Pulmonary infection (PI) remains a prevalent and severe complication in patients recovering from spontaneous deep subcortical intracerebral hemorrhage (deep SICH). Accurate prediction of PI risk is crucial for ...
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