信息技术在医院病案统计中的应用

伴随着我国综合实力的不断提升,信息技术获得了突飞猛进的发展,如今,医疗改革如火如荼地进行,医院的信息管理工作同以往相比较也实现了极大的发展。病案统计工作是指相关工作人员对医院中患者的信息进行全面统计,若是这项工作完成得足够出色,会给医院后续的信息查询工作带来诸多方便和快捷,也能够在某种程度上使医院管理效率和质量实现一定幅度的提升。因此,将信息技术和医院的病案工管理工作进行密切结合,就成为病案管理人员应当重点思考的问题。病案统计工作是医院管理工作中的一个重点环节,病案统计结果能够为医院整体绩效带来十分深远的影响。因此,投入更多时间和精力来提高对这项工作的重视,显得十分关键和重要。文章对信息技术在医院病案统计中的应用展开了分析。     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.     

Effect of resiliency and age on musculoskeletal injuries and lost workdays in emergency medical service personnel

Emergency medical service (EMS) personnel are highly skilled health care professionals who often provide lifesaving clinical care to patients. Paradoxically, they may be repeatedly exposed to a unique set of occupational hazards that could endanger their own health. This cross-sectional study sought to examine the relation between resiliency and musculoskeletal injuries (MSIs) and between resiliency and lost workdays due to MSIs, and explore whether age modifies these associations. Multivariable Poisson main effects regression models showed that resiliency had a protective effect against MSIs, but not lost workdays. In the unadjusted regression model to evaluate the relation between resiliency and age, results suggested that no differences in distributions existed between younger and older EMS personnel and resiliency. However, given the same unit increase in resiliency, findings from multivariable Poisson interaction regression models indicated that older workers had a higher prevalence of MSIs and lost workdays than younger workers. Results from main effects models may reflect diverging routes on a pathophysiological pathway, in which resiliency acts as a prognostic factor for MSIs but not lost workdays. Findings might also indicate the association between resiliency, and MSIs and lost workdays varies by age.Relevance to industryThe largest growth of labor in the US is expected to occur in the oldest segments of the population. While older workers may offer more experience and show similar resiliency to younger workers, they might be more vulnerable to individual risk factors and occupational exposures. If management wants to retain older workers as assets, they should design the work environment to match the capabilities of all workers.     

An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.     

Dealing with the Foreign-Body Response to Implanted Biomaterials: Strategies and Applications of New Materials

Foreign-body response caused by implanted biomaterials seriously impedes the function of implants and is a major obstacle to the development of implantable biomaterials and medical devices. Recent advances in implantable biomaterials and medical devices have provided strategies to resist the foreign-body response. In this review, the mechanism of the foreign-body response and conventional strategies to mitigate foreign-body response is briefly introduced. Then, three types of promising foreign-body response resisting materials are focused and the advantages, characteristics, and applications of each material are discussed. Finally, prospects are put forward for future development of foreign-body response resisting materials and current challenges that require in-depth study.     

Using a hybrid two‐stage DEA model to analyze the profitability and marketability of medical manufacturing firms

The purpose of this study is to develop a modification of the model developed by Chen and Zhu in 2004. Calculating stage and overall efficiencies precisely and consistently has become a major challenge of the two‐stage DEA model. However, most other models do not calculate the optimality of intermediates. Although the model developed by Chen and Zhu measures the optimality of intermediates, the calculated efficiency scores still have some shortfalls. The modified model, named the hybrid two‐stage DEA model, fills the gap between calculating the optimality of intermediates and the consistency of overall efficiency scores. In addition to obtaining an accurate measurement for the optimality of intermediates, the model confines efficiency scores to a range from zero to one (a ratio efficiency score). In an empirical evaluation, we use data from 64 medical manufacturing firms to test the performance of the hybrid model and offer recommendations for the industry.     

推进实施广东省创新医疗器械产品应用示范工程的对策建议

通过对参与广东省“十百千万工程”政商学医各界，包括广东省科学技术厅、广东省食品药品监督管理局、深圳迈瑞生物医疗电子股份有限公司、广州三瑞医疗器械有限公司、汕头市超声仪器研究所有限公司、金域检验中心有限公司、中山大学达安健康产业集团、广东省医疗器械产业技术创新战略联盟、南方医科大学、中山大学工学院、中科院深圳先进技术研究院、韶关乐昌市乐城街道社区卫生服务中心、梅州市蕉岭县中医医院、梅州市蕉岭县新铺镇中心卫生院、蕉岭县广福镇中心卫生院、梅州市兴宁市坭陂镇中心卫生院共16家单位进行调研，分析了实施广东省创新医疗器械产品应用示范工程的意义、现状及问题，提出了进一步推动该项工程的对策建议。     

基于仿真技术的血管支架工艺设置的研究

医用冠脉血管支架加工工艺复杂，工艺条件的设置对支架的性能有较大的影响。为清晰的了解压握、扩张尺寸设置对支架性能的影响，利用计算机仿真技术研究不同工艺设置对支架性能的影响。通过研究发现：压握工况设置下的支架安全性能随压握尺寸减小而变差；扩张工况设置下的支架安全性能随扩张尺寸减小而变好；并且不同的压握、扩张条件对支架的应力也产生一定的影响。结果表明：可以利用仿真技术预见不同的工艺设置对支架的性能影响，完善工艺设置。     

Review of the environmental policy instruments in the standardization of H2 for the decarbonization of Mexico

The objective of this work is to review the environmental and ecological policy instruments that Mexican legislation for existence of an Official Mexican Hydrogen Standard according to the premises motivates decarbonization. There is a worldwide interest in hydrogen (H₂) and its incorporation into legal systems. In Mexico there are large legal gaps that question the legality of H₂, because it is not recognized in a federal law and is only considered in technical standards of voluntary application. Also, Mexico has forest, agricultural, and livestock waste that, if properly used, can cover the energy demand of communities. The main findings of this study refer to the fact that, according to international standards for the management of H₂ and taking into account the regulatory limitations in Mexico, it is possible to promote, an Official Mexican Standard exclusively aimed at establishing the minimum requirements for the design, construction, operation and maintenance of H₂ storage, considering the areas of the opportunity offered by foreign experiences, according to the Mexican context. It is concluded that the parameters of legality exist in the instruments of environmental policy, to support the existence of an Official Mexican Standard of H₂.     

重庆市药物临床试验机构现状分析及地区性机构体系构建的思考

目的：重庆市具有丰富的医药产业底蕴和极佳的医疗资源，在我国药品注册审评制度改革紧密推进和重庆市大力发展生物医药产业的大背景下，本课题从对重庆市药物临床试验机构（以下简称机构）管理人员调研的角度入手，旨在明确重庆市机构建设现状与问题，探讨可行的创新发展对策。方法：本研究对我市20家有药物临床试验资质机构的54位管理人员问卷数据进行统计；并通过比对相应的公共数据，系统分析我市机构建设水平。结果：问卷分析显示，我市机构仍以传统GCP架构模式为主；以承担国内企业项目为主（90.00%），65.00%的机构近三年承接项目总数在40项以下；机构管理者多数从事机构相关工作的工作年限多在6年以内（79.59%），且仍有部分机构管理者未接受系统的培训；对公共数据调查分析显示，截至2019年5月底，我市机构总数目为22家，自2017年7月22日以来承接在Clinical Trails注册的在研项目数为81项，远低于机构工作开展较好的城市（北京：61家，433项；上海：53家，326项）。结论：重庆市机构建设水平亟待提高，应整合全市临床研究资源，构建区域化的药物临床试验机构体系，加速重庆市医药产业的创新发展。