全文获取类型
收费全文 | 180391篇 |
免费 | 13884篇 |
国内免费 | 697篇 |
专业分类
耳鼻咽喉 | 2245篇 |
儿科学 | 4625篇 |
妇产科学 | 3734篇 |
基础医学 | 23899篇 |
口腔科学 | 4082篇 |
临床医学 | 18811篇 |
内科学 | 35676篇 |
皮肤病学 | 2323篇 |
神经病学 | 15552篇 |
特种医学 | 7346篇 |
外国民族医学 | 1篇 |
外科学 | 27620篇 |
综合类 | 2752篇 |
现状与发展 | 1篇 |
一般理论 | 195篇 |
预防医学 | 17429篇 |
眼科学 | 3981篇 |
药学 | 12939篇 |
4篇 | |
中国医学 | 232篇 |
肿瘤学 | 11525篇 |
出版年
2022年 | 1107篇 |
2021年 | 3172篇 |
2020年 | 1799篇 |
2019年 | 2984篇 |
2018年 | 3610篇 |
2017年 | 2702篇 |
2016年 | 2802篇 |
2015年 | 3361篇 |
2014年 | 4877篇 |
2013年 | 7430篇 |
2012年 | 10843篇 |
2011年 | 11507篇 |
2010年 | 6353篇 |
2009年 | 5716篇 |
2008年 | 10115篇 |
2007年 | 10844篇 |
2006年 | 10423篇 |
2005年 | 10559篇 |
2004年 | 10062篇 |
2003年 | 9416篇 |
2002年 | 9254篇 |
2001年 | 2856篇 |
2000年 | 2732篇 |
1999年 | 2812篇 |
1998年 | 2221篇 |
1997年 | 1778篇 |
1996年 | 1733篇 |
1995年 | 1712篇 |
1994年 | 1446篇 |
1993年 | 1431篇 |
1992年 | 2083篇 |
1991年 | 2016篇 |
1990年 | 1885篇 |
1989年 | 1793篇 |
1988年 | 1731篇 |
1987年 | 1722篇 |
1986年 | 1661篇 |
1985年 | 1732篇 |
1984年 | 1604篇 |
1983年 | 1476篇 |
1982年 | 1551篇 |
1981年 | 1528篇 |
1980年 | 1355篇 |
1979年 | 1281篇 |
1978年 | 1176篇 |
1977年 | 999篇 |
1976年 | 910篇 |
1975年 | 836篇 |
1974年 | 915篇 |
1973年 | 871篇 |
排序方式: 共有10000条查询结果,搜索用时 937 毫秒
41.
42.
A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross‐covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity. 相似文献
43.
44.
45.
46.
47.
48.
Xinran Liu James Anstey Ron Li Chethan Sarabu Reiri Sono Atul J. Butte 《Applied clinical informatics》2021,12(2):407
Background Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers. 相似文献
49.
50.
Molly Orcutt Wendy C. King Melissa A. Kalarchian Michael J. Devlin Marsha D. Marcus Luis Garcia Kristine J. Steffen James E. Mitchell 《Surgery for obesity and related diseases》2019,15(2):295-303