Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management [0.03%]
基于机器学习的诱导后低血压预测：识别风险因素并改善麻醉管理
Ming Chen,Dingyu Zhang
Ming Chen
Background: Post-induction hypotension (PIH) increases surgical complications including myocardial injury, acute kidney injury, delirium, stroke, prolonged hospitalization, and endangerment of the patient's life. Machine ...
User acceptability and perceived impact of a mobile interactive education and support group intervention to improve postnatal health care in northern India: a qualitative study [0.03%]
在印度北部提高产后期保健的移动互动教育和支持小组干预措施的用户接受度和感知影响：一项定性研究
Valentina Cox,Preetika Sharma,Garima Singh Verma et al.
Valentina Cox et al.
Background: Postnatal care, crucial for preventing and assessing complications after birth, remains low in India. An interactive mHealth community-based postnatal intervention was implemented to promote healthy maternal b...
Decision tree model for predicting ovarian tumor malignancy based on clinical markers and preoperative circulating blood cells [0.03%]
基于临床标志物和术前循环血液细胞的卵巢肿瘤恶性预测决策树模型
Yingjia Li,Xingping Zhao,Yanhua Zhou et al.
Yingjia Li et al.
Objective: Ovarian cancer is a serious malignant tumor threatening women's health. The early diagnosis and effective treatments of ovarian cancer remain inadequate, and about 70% of ovarian cancers are in advanced stages ...
Intracranial stenosis prediction using a small set of risk factors in the Tromsø Study [0.03%]
特罗姆瑟研究中利用少量的危险因素预测颅内动脉狭窄
Luca Bernecker,Liv-Hege Johnsen,Torgil Riise Vangberg
Luca Bernecker
Intracranial atherosclerotic stenosis (ICAS) refers to a narrowing of intracranial arteries due to plaque buildup on the inside of the vessel walls restricting blood flow. Early detection of ICAS is crucial to prevent serious consequences s...
Modeling-based design of adaptive control strategy for the effective preparation of 'Disease X' [0.03%]
基于模型的自适应控制策略设计以有效应对'Disease X'的挑战
Hao Wang,Weike Zhou,Xia Wang et al.
Hao Wang et al.
This study aims at exploring a general and adaptive control strategy to confront the rapid evolution of an emerging infectious disease ('Disease X'), drawing lessons from the management of COVID-19 in China. We employ a dynamic model incorp...
Uncovering the potential of smartphones for behavior monitoring during migraine follow-up [0.03%]
智能手机在偏头痛随访中行为监测的潜力探究
Marija Stojchevska,Jonas Van Der Donckt,Nicolas Vandenbussche et al.
Marija Stojchevska et al.
Background: Migraine is a neurological disorder that affects millions of people worldwide. It is one of the most debilitating disorders which leads to many disability-adjusted life years. Conventional methods for investig...
Deep learning-based automated guide for defining a standard imaging plane for developmental dysplasia of the hip screening using ultrasonography: a retrospective imaging analysis [0.03%]
基于深度学习的自动化引导技术在超声筛查婴幼儿髋关节发育不良中确定标准成像平面的应用：一项回顾性影像学分析
Kyung-Sik Ahn,Ji Hye Choi,Heejou Kwon et al.
Kyung-Sik Ahn et al.
Background: We aimed to propose a deep-learning neural network model for automatically detecting five landmarks during a two-dimensional (2D) ultrasonography (US) scan to develop a standard plane for developmental dysplas...
Diabetic peripheral neuropathy detection of type 2 diabetes using machine learning from TCM features: a cross-sectional study [0.03%]
基于中医特征运用机器学习进行二型糖尿病周围神经病变检测的横断面研究
Zhikui Tian,JiZhong Zhang,Yadong Fan et al.
Zhikui Tian et al.
Aims: Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes mellitus. Early identification of individuals at high risk of DPN is essential for successful early intervention. Traditional Chinese ...
Workload in antenatal care before and after implementation of an electronic decision support system: an observed time-motion study of healthcare providers in Nepal [0.03%]
在尼泊尔电子决策支持系统实施前后产前护理中的工作负荷：对卫生保健人员的现场工作效率研究
Emma Radovich,Seema Das,Sulata Karki et al.
Emma Radovich et al.
Background: Healthcare interventions are shaped by the resources needed to implement them, including staff time. This study, part of a process evaluation, aims to compare time spent on antenatal care (ANC) and related rec...
Observational Study
BMC medical informatics and decision making. 2025 Feb 18;25(1):87. DOI:10.1186/s12911-025-02868-1 2025
How good is your synthetic data? SynthRO, a dashboard to evaluate and benchmark synthetic tabular data [0.03%]
你的合成数据有多好？SynthRO，一个评估和基准化合成表格数据的仪表板
Gabriele Santangelo,Giovanna Nicora,Riccardo Bellazzi et al.
Gabriele Santangelo et al.
Background: The exponential growth in patient data collection by healthcare providers, governments, and private industries is yielding large and diverse datasets that offer new insights into critical medical questions. Le...