全文获取类型
收费全文 | 88篇 |
免费 | 13篇 |
专业分类
儿科学 | 1篇 |
妇产科学 | 1篇 |
基础医学 | 17篇 |
口腔科学 | 1篇 |
临床医学 | 12篇 |
内科学 | 6篇 |
皮肤病学 | 2篇 |
神经病学 | 6篇 |
特种医学 | 4篇 |
外科学 | 15篇 |
综合类 | 7篇 |
预防医学 | 12篇 |
眼科学 | 2篇 |
药学 | 8篇 |
中国医学 | 2篇 |
肿瘤学 | 5篇 |
出版年
2023年 | 8篇 |
2022年 | 2篇 |
2021年 | 7篇 |
2020年 | 9篇 |
2019年 | 7篇 |
2018年 | 8篇 |
2017年 | 5篇 |
2016年 | 5篇 |
2015年 | 8篇 |
2014年 | 7篇 |
2013年 | 8篇 |
2012年 | 2篇 |
2011年 | 2篇 |
2010年 | 1篇 |
2009年 | 1篇 |
2008年 | 9篇 |
2007年 | 2篇 |
2006年 | 3篇 |
2005年 | 1篇 |
2004年 | 1篇 |
2003年 | 2篇 |
2001年 | 3篇 |
排序方式: 共有101条查询结果,搜索用时 46 毫秒
1.
2.
3.
4.
5.
针对肥厚型心肌病和扩张型心肌病患者的心电图导联间的相关性,提出心肌病自动诊断的一种方法。该研究从12导联ECG信号中分割出来的单个心跳片段进行识别,以健康人群为对照识别出DCM和HCM的片段。从片段中提取264个非参数相关系数特征并通过变量筛选得到12个特征,输入到支持向量机中进行建模,采用10折交叉验证评价模型。模型的总准确率为99.88%±0.08%。模型使用的特征少,运行速度快,准确率高,有助于临床心肌病的自动化诊断,节约医疗资源。 相似文献
6.
目的 构建颈动脉斑块超声图像数据集并探讨深度学习技术对颈动脉斑块自动分类诊断的应用价值。方法 首先采集254例患者和354例正常人的颈部动脉超声图像,每例采集2幅图像,构建共包含1216幅图像的颈动脉超声图像数据集;然后,基于已构建的颈动脉超声图像数据集对传统的HOG+SVM方法和14种不同结构的深度神经网络模型进行训练;最后,通过三个量化指标(分类精确率、召回率和F1值)确定现有的颈动脉斑块超声图像分类性能最好的深度神经网络模型。 结果 通过综合比较15种不同的颈动脉斑块超声图像分类方法,得出性能最好的模型是深度残差网络模型ResNet50,其精确率、召回率和F1值分别为97.36%、97.32%和97.34%。 结论 本文通过数据集构建、模型选择、模型训练和测试验证了深度学习技术在颈动脉斑块超声图像自动诊断应用中的有效性,其中深度残差网络模型ResNet50对颈动脉超声图像能进行高准确度自动分类。 相似文献
7.
Richard A. Deyo Samuel F. Dworkin Dagmar Amtmann Gunnar Andersson David Borenstein Eugene Carragee John Carrino Roger Chou Karon Cook Anthony DeLitto Christine Goertz Partap Khalsa John Loeser Sean Mackey James Panagis James Rainville Tor Tosteson Dennis Turk Debra K. Weiner 《The journal of pain》2014,15(6):569-585
Despite rapidly increasing intervention, functional disability due to chronic low back pain (cLBP) has increased in recent decades. We often cannot identify mechanisms to explain the major negative impact cLBP has on patients' lives. Such cLBP is often termed non-specific and may be due to multiple biologic and behavioral etiologies. Researchers use varied inclusion criteria, definitions, baseline assessments, and outcome measures, which impede comparisons and consensus. Therefore, NIH Pain Consortium charged a Research Task Force (RTF) to draft standards for research on cLBP. The resulting multidisciplinary panel recommended using 2 questions to define cLBP; classifying cLBP by its impact (defined by pain intensity, pain interference, and physical function); use of a minimum dataset to describe research participants (drawing heavily on the PROMIS methodology); reporting “responder analyses” in addition to mean outcome scores; and suggestions for future research and dissemination. The Pain Consortium has approved the recommendations, which investigators should incorporate into NIH grant proposals. The RTF believes that these recommendations will advance the field, help to resolve controversies, and facilitate future research addressing the genomic, neurologic, and other mechanistic substrates of chronic low back pain. We expect that the RTF recommendations will become a dynamic document and undergo continual improvement.PerspectiveA task force was convened by the NIH Pain Consortium with the goal of developing research standards for chronic low back pain. The results included recommendations for definitions, a minimum dataset, reporting outcomes, and future research. Greater consistency in reporting should facilitate comparisons among studies and the development of phenotypes. 相似文献
8.
9.
AimDuring 2008–2011 Australian Coding Standards mandated a causal relationship between diabetes and inpatient care as a criterion for recording diabetes as a comorbidity in hospital administrative datasets. We aim to measure the effect of the causality mandate on recorded diabetes and associated inter-hospital variations.MethodFor patients with diabetes, all admissions between 2004 and 2013 to all New South Wales acute public hospitals were investigated. Poisson mixed models were employed to derive adjusted rates and variations.ResultsThe non-recorded diabetes incidence rate was 20.7%. The causality mandate increased the incidence rate four fold during the change period, 2008–2011, compared to the pre- or post-change periods (32.5% vs 8.4% and 6.9%). The inter-hospital variation was also higher, with twice the difference in the non-recorded rate between hospitals with the highest and lowest rates (50% vs 24% and 27% risk gap). The variation decreased during the change period (29%), while the rate continued to rise (53%). Admission characteristics accounted for over 44% of the variation compared with at most two per cent attributable to patient or hospital characteristics. Contributing characteristics explained less of the variation within the change period compared to pre- or post-change (46% vs 58% and 53%). Hospital relative performance was not constant over time.ConclusionThe causality mandate substantially increased the non-recorded diabetes rate and associated inter-hospital variation. Longitudinal accumulation of clinical information at the patient level, and the development of appropriate adoption protocols to achieve comprehensive and timely implementation of coding changes are essential to supporting the integrity of hospital administrative datasets. 相似文献
10.
Carlos Sez Nekane Romero J Alberto Conejero Juan M García-Gmez 《J Am Med Inform Assoc》2021,28(2):360
ObjectiveThe lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning.Materials and MethodsWe used the publicly available nCov2019 dataset, including patient-level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities.ResultsCases from the 2 countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting.ConclusionsData source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning. 相似文献