论高校体育情报特点与标准建设的意义

高校体育情报的特点分别为信息模式的数字化、信息内容的时效性、信息服务的针对性、信息来源的多元化、信息内容的校本化;结合高校体育情报特点阐述高校体育情报标准建设的必要性,以促进高校体育教学主客体发展、提升高校体育教育实施效率、加快高校体育远程教学的实现三个主题内容做出阐释.     

从国外经验看我国医学生的医患沟通教育

科学技术飞速发展的社会充满了机遇与挑战,也对人才提出了更高的要求。当代医学生不仅要具有专业理论知识和专业技能,还必须具备较高的情商水平才能适应现代医学发展的需要。这就要求医学院校重视和加强医学生的情商教育,只有这样才能培养出真正的合格人才。     

三维ResNet 网络预测肺腺癌结节亚型的效能及其 稳定性

目的:探究ResNet模型对肺腺癌不同亚型结节的分类表现及稳定性。方法:回顾性收集2014 年2 月—2020 年10 月期 间的364 例肺腺癌结节CT 影像数据,以7∶3 的比例分为训练集和内部测试集,将2020 年4 月到2020 年11 月的58 例结节数 据作为外部测试集。使用基于ResNet的三维卷积神经网络在训练集中进行训练以及调参,并使用内部测试集和外部测试集对 模型的准确性及泛化性进行评估。使用随机中心移动和掩膜处理的方式分别以内部测试集和外部测试集为基础构造新的测试 集,新数据集对模型进行测试验证模型的稳定性。结果:模型在内部测试集AUC 为0.949 1（95%CI:0.910 8～0.987 4）,模型在随 机中心移动以及掩膜处理之后的数据集的AUC 值分别为0.940 4 和0.918 1, 与其差异无统计学意义（P 值分别为0.425 3 和 0.239 3）。在外部测试集中模型AUC 为0.959 6（95%CI:0.901 2～1.000 0）,在用于稳定性测试的随机中心移动以及掩膜处理之 后的数据集中,模型所得AUC 分别为0.948 5和0.947 3,与其同样差异无统计学意义（均P>0.05）。结论:ResNet 模型对肺腺癌 结节亚型有优异的鉴别能力,并且具有一定稳定性。     

“人工智能+X”背景下医学信息管理专业研究生培养模式

阐述当前“人工智能+X”背景下市场对医学信息管理专业人才能力的需求，分析医学信息管理专业人才培养现状，提出从重塑学科人才培养目标、优化课程内容与课程设置、建设“双师型”导师队伍、搭建多方协同共建共享在线平台及设立“政用产学研”联合培养基地等方面探索医学信息管理专业研究生培养模式，以期培养适应人工智能时代发展，具备学科优势特色的高层次、高水平、高质量的复合型、应用型、创新型人才。     

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people worldwide. With the recent rise of new Delta and Omicron variants, the efficacy of the vaccines has become an important question. The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions, particularly for healthcare workers. In this paper, we discuss the current literature on invasive/contact and non-invasive/non-contact technologies (including Wi-Fi, radar, and software-defined radio) that have been effectively used to detect, diagnose, and monitor human activities and COVID-19 related symptoms, such as irregular respiration. In addition, we focused on cutting-edge machine learning algorithms (such as generative adversarial networks, random forest, multilayer perceptron, support vector machine, extremely randomized trees, and k-nearest neighbors) and their essential role in intelligent healthcare systems. Furthermore, this study highlights the limitations related to non-invasive techniques and prospective research directions.     

护士情绪智力与应对方式的相关性研究

目的 考察护士情绪智力水平以及应对方式的特征,探讨情绪智力在职业压力应对中的作用.方法 采用整群随机抽样的方法,使用情绪智能量表(WEIS)和护士应对方式问卷对重庆市5家综合医院的464名护士进行调查;应用Logistic回归模型来评价护士背景因素和情绪智力对职业压力应对方式的影响.结果 (1)护士情绪智力水平和压力应对方式受到人口学变量的影响,但在学历层次上情绪智力组间差异无显著性(P＞0.05).(2)护士较多使用问题解决[(2.60±0.55)分]和正面认识[(2.60±0.75)分]的应对方式,很少采用回避与自责[(1.37±0.69)分]的方式应对职业压力.(3)护士的情绪智力与职业压力应对方式存在相关性(r=-0.123～0.253,P＜0.01;P＜0.05).结论 情绪智力的四个能力维度分别对护士职业压力的六种应对方式有不同程度的预测作用.     

Will teledermatology be the silver lining during and after COVID‐19?

The novel coronavirus disease (COVID‐19) has limited traditional consultation and minimized health care access. Teledermatology (TD) has come to the rescue in this situation by extending consultation for nonessential conditions to the comfort of patient's homes. This limits the risk of exposure of both doctors and patients to the coronavirus (SARS‐CoV‐2). And while there is a reported increase in teleconsultations during the ongoing pandemic, there are some demerits that avert the shift to virtualized health care. The authors conducted an online survey to further understand the hesitancy, limitations, merits, and the demographic of dermatologists who were conducive to TD and these data were analyzed and presented in this article. While TD might never replace physical consultation, it definitely serves an adjunctive role in the post‐COVID era, provided adequate regulatory measures are in place.     

人工智能对基层影像医生筛查肺结节的辅助价值

目的探讨基层影像医生结合人工智能辅助诊断系统对厚层CT中4 mm以上结节检出效能的差异。方法前瞻性收集2019年1月1日至1月31日在我院接受常规胸部CT检查的118例患者并进行层厚5 mm骨算法重建,由两位十年以上诊断经验的主治医师和一位十五年以上诊断经验的副主任医师借助人工智能软件(InferRead CT_Lung 6.0,Infervision,Beijing,推想科技)确定肺结节金标准。由基层医院两位主治医师对CT图像进行独立阅片,记录结节的数量、位置、长径和标记时间,两周后再次借助AI对同一批图像进行阅片,计算医生独立阅片(A组)及借助AI(B组)两种情况下的结节检测敏感度、假阳性率,同时比较两种情况标记时间。结果A组和B组对于4 mm以上肺结节检出总数分别为172和293个,其中真阳性结节数分别为112和171个,假阳性结节数分别为60和122个。B组肺结节的检测敏感度显著高于A组(P0.01)。A、B两组医生肺结节平均检测时间比较,差异有统计学意义(P0.05)。结论 AI对于肺结节检出具有较好的辅助能力,借助AI辅助诊断系统的基层影像医生对厚层图像4 mm以上肺结节的标记时间更短,结节诊断敏感度更高。     

机器学习在放射治疗中的临床应用

放射治疗是癌症的主要治疗手段之一,以机器学习为代表的人工智能飞速发展,可应用于放射治疗临床实践的各个环节,包括临床决策支持、自动勾画靶区、预测疗效和副反应等,提高准确性与效率.尽管面临着结构化数据缺乏、模型可解释性差等挑战,机器学习在放射治疗中的应用将日趋深刻而广泛.本文从机器学习简介、在放射治疗中的临床应用研究进展和...     

Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma

Background Dermoscopy improves diagnostic accuracy of the unaided eye for melanoma, and digital dermoscopy with artificial intelligence or computer diagnosis has also been shown useful for the diagnosis of melanoma. At present there is no clear evidence regarding the diagnostic accuracy of dermoscopy compared with artificial intelligence. Objectives To evaluate the diagnostic accuracy of dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis and to compare the diagnostic accuracy of the different dermoscopic algorithms with each other and with digital dermoscopy/artificial intelligence for the detection of melanoma. Methods A literature search on dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis was performed using several databases. Titles and abstracts of the retrieved articles were screened using a literature evaluation form. A quality assessment form was developed to assess the quality of the included studies. Heterogeneity among the studies was assessed. Pooled data were analysed using meta‐analytical methods and comparisons between different algorithms were performed. Results Of 765 articles retrieved, 30 studies were eligible for meta‐analysis. Pooled sensitivity for artificial intelligence was slightly higher than for dermoscopy (91% vs. 88%; P = 0·076). Pooled specificity for dermoscopy was significantly better than artificial intelligence (86% vs. 79%; P < 0·001). Pooled diagnostic odds ratio was 51·5 for dermoscopy and 57·8 for artificial intelligence, which were not significantly different (P = 0·783). There were no significance differences in diagnostic odds ratio among the different dermoscopic diagnostic algorithms. Conclusions Dermoscopy and artificial intelligence performed equally well for diagnosis of melanocytic skin lesions. There was no significant difference in the diagnostic performance of various dermoscopy algorithms. The three‐point checklist, the seven‐point checklist and Menzies score had better diagnostic odds ratios than the others; however, these results need to be confirmed by a large‐scale high‐quality population‐based study.