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.     

Structure of presented stimuli influences gazing behavior and choice

Well-structured stimuli presentation is essential in eye-tracking research to test predefined hypotheses reliably and to conduct relevant gazing behavior studies. Several bottom-up factors associated with stimuli presentation (such as stimuli orientation, size etc.) can influence gazing behavior. However, only a small number of scientific papers address these factors in a sensory and consumer science context and thus provide guidance to practitioners. The two presented eye-tracking studies on food images aimed at evaluating the effect of the bottom-up factors stimulus size, background of the picture, orientation of food product presentation, the evaluated products and the number of alternatives. Significant effects of product group were found in the case of all eye-movement parameters except time to first fixation and first fixation duration. In contrary, orientation significantly influenced only the time to first fixation and first fixation duration parameters. Stimulus size significantly increased fixation and dwell count, while background showed no significant effects. Furthermore, significant relationships were found between the number of presented images and eye-movement and decision time. Less time was needed in 2AFC (alternative forced choice test), 3AFC and 4AFC and significantly more time was needed to choose one alternative out of 7AFC and 8AFC. The results of the two studies show that the investigated bottom-up factors can significantly influence gazing behavior, and therefore need to be carefully considered when planning or comparing results of eye-tracking experiments.     

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。