找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Lehrerprofessionalität und die Qualität von Mathematikunterricht; Quantitative Studien Michael Besser Book 2014 Springer Fachmedien Wiesbad

[复制链接]
楼主: IU421
发表于 2025-3-23 12:13:45 | 显示全部楼层
发表于 2025-3-23 17:34:00 | 显示全部楼层
Michael Besserdvent of Big Data in the healthcare arena, such that real-time data are now available to assist many clinical decisions. Real World Data (RWD) from hospital information system structured numerical data and unstructured text data, and it is imperative that phenotyping reproducibly extracts patients w
发表于 2025-3-23 19:57:05 | 显示全部楼层
发表于 2025-3-24 01:47:14 | 显示全部楼层
Michael Besserl in person is limited in Peru. The objective of the research was to evaluate the influence of a telehealth intervention on the knowledge of danger signs in pregnancy, childbirth and postpartum in pregnant women during the health emergency due to COVID-19. A quasi-experimental research was carried o
发表于 2025-3-24 04:35:58 | 显示全部楼层
发表于 2025-3-24 07:53:26 | 显示全部楼层
Michael Besserto help doctors and nurses save the life of a newborn whose respiratory circulation is unstable immediately after birth. Workshops are held throughout Japan consisting of lectures, scenario training, and review in support of this goal. In the NCPR workshop, it is recommended to review student activi
发表于 2025-3-24 12:00:07 | 显示全部楼层
发表于 2025-3-24 18:28:26 | 显示全部楼层
Michael Bessereting risk from stroke survival data, which enables to identify several types of events over the follow-up time for each patient affected by stroke. We explore the possibilities of recovery or death from stroke complications by exploring medical data of the neurology department. Our main interest is
发表于 2025-3-24 20:41:46 | 显示全部楼层
Michael Besserment and prediction. Deep neural networks (DNNs) are appealing for survival analysis because of their non-linear nature. However, DNNs are often described as “black box” models because they are hard or practically impossible to explain. In this study, we propose an explainable deep network framework
发表于 2025-3-25 01:45:11 | 显示全部楼层
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-6-29 02:12
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表