|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Clinical Big Data and Deep Learning:Applications,Challenges,and Future Outlooks显示文摘The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. Firstly,based on the analysis of the characteristics of clinical data, various types of clinical data(e.g., medical images,clinical notes, lab results, vital signs, and demographic informatics) are discussed and details provided of some public clinical datasets. Secondly, a brief review of common deep learning models and their characteristics is conducted. Then, considering the wide range of clinical research and the diversity of data types, several deep learning applications for clinical data are illustrated: auxiliary diagnosis, prognosis, early warning, and other tasks.Although there are challenges involved in applying deep learning techniques to clinical data, it is still worthwhile to look forward to a promising future for deep learning applications in clinical big data in the direction of precision medicine. | Ying Yu Min Li Liangliang Liu Yaohang Li Jianxin Wang | 2019 | Big Data Mining and Analytics2019，2，4： | 4 |
| 2 | Big Data Analytics in Healthcare: Data-Driven Methods for Typical Treatment Pattern Mining显示文摘A huge volume of digitized clinical data is generated and accumulated rapidly since the widespread adoption of Electronic Medical Records (EMRs).These big data in healthcare hold the promise of propelling healthcare evolving from a proficiency-based art to a data-driven science,from a reactive mode to a proactive mode,from one-size-fits-all medicine to personalized medicine.This paper first discusses the research background-big data analytics in healthcare,the research framework of big data analytics in healthcare,analysis of medical process,and the literature summary of treatment pattern mining.Then the challenges for data-driven typical treatment pattern mining are highlighted,including similarity measure between treatment records,typical treatment pattern extraction,evaluation and recommendation,when considering the rich temporal and heterogeneous medical information in EMRs.Furthermore,three categories of typical treatment patterns are mined from doctor order content,duration,and sequence view respectively,which can provide a data-driven guideline to achieve the '5R' goal for rational drug use and clinical pathways. | Chonghui Guo Jingfeng Chen | 2019 | Journal of Systems Science and Systems Engineering2019，28，6： | 3 |
| 3 | Application status and development of big data in medical education in China显示文摘With the development of information technology, big data has been widely used in medical education in China. Through the analysis of the definition, characteristics and development process of big data, summarized the transformation of domestic big data medical education mode compared with the traditional medical education mode in the teaching mode reform, research method innovation, teaching courses optimization, teaching key extension and the teaching quality monitoring. Based on this, this paper expounds the impact and challenge of big data on medical education, and makes an outlook on its development prospect, indicating that the future development of big data will be open, popular and trending. At the same time, some suggestions on further optimization direction of China's big data medical education are put forward. | Ke-Jia Liu Yu-Di Cao Yue Hu Li-Jiao Wei | 2019 | Medical Data Mining2019，2，3： | 2 |
| 4 | Implicit Shape Reconstruction of Unorganized Points Using PDE-Based Deformable 3D Manifolds显示文摘In this work we consider the problem of shape reconstruction from an unorganized data set which has many important applications in medical imaging, scientific computing, reverse engineering and geometric modelling. The reconstructed surface is obtained by continuously deforming an initial surface following the Partial Differential Equation (PDE)-based diffusion model derived by a minimal volume-like variational formulation. The evolution is driven both by the distance from the data set and by the curvature analytically computed by it. The distance function is computed by implicit local interpolants defined in terms of radial basis functions. Space discretization of the PDE model is obtained by finite co-volume schemes and semi-implicit approach is used in time/scale. The use of a level set method for the numerical computation of the surface reconstruction allows us to handle complex geometry and even changing topology,without the need of user-interaction. Numerical examples demonstrate the ability of the proposed method to produce high quality reconstructions. Moreover, we show the effectiveness of the new approach to solve hole filling problems and Boolean operations between different data sets. | Elena Franchini Serena Morigi Fiorella Sgallari | 2010 | Numerical Mathematics(Theory,Methods and Applications)2010，3，4： | 2 |
| 5 | Demand Analysis and Management Suggestion:Sharing Epidemiological Data Among Medical Institutions in Megacities for Epidemic Prevention and Control显示文摘During the prevention of coronavirus disease 2019(COVID-19),epidemiological data is essential for controlling the source of infection,cutting off the route of transmission,and protecting vulnerable populations.Following Law of the People's Republic of China on Prevention and Treatment of Infectious Diseases and other related regulations,medical institutions have been authorized to collect the detailed information of patients,while it is still a formidable task in megacities because of the significant patient mobility and the existing information sharing barrier.As a smart city which strengthens precise epidemic prevention and control,Shanghai has established a multi-department platform named'one-net management'on dynamic information monitoring.By sharing epidemiological data with medical institutions under a safe environment,we believe that the ability to prevent and control epidemics among medical institutions will be effectively and comprehensively improved. | 蔡沁怡 宓轶群 储昭武 郑元义 陈方 刘义成 | 2020 | Journal of Shanghai Jiaotong university(Science)2020，25，2： | 1 |
| 6 | Practice and Exploration of Economic Benefit Evaluation of Medical Equipment in Our Hospital显示文摘Objective The evaluation index of medical equipment's economic benefit is based on the usage of medical equipment,the traditional data collection method is time-consuming,laborious and not entirely accurate.The usage of medical equipment is obtained by designing data query statements from the HIS system.Methods First the charging items are in correspondence with the device's name included,second fees and other relevant data are extracted from charging module in HIS.Through a rough estimate of the recovery period and an increase or decrease ratio,the economic benefit of the medical equipment can be analyzed.Results Through the method of the benefit analysis of the medical equipment,we can clearly find out the different economic benefit of the equipment,and finally analyze the reasons.Conclusion Practice has proved that,this methad,it can greatly reduce human,material resources required in data collection and improve the accuracy of the data.It can help hospital managers timely to grasp the operating costs of medical equipment and other information,and also provide scientific data for hospital managers when they purchase reasonable medical equipment. | Wei-wei Shi | 2016 | 中国医疗设备2016，31，12： | 1 |
| 7 | Legal challenges for the implementation of advanced clinical digital decision support systems in Europe显示文摘Systems based on artificial intelligence and machine learning that facilitate decision making in health care are promising new tools in the era of‘personalized’or‘precision’medicine.As the volume of patient data and scientific evidence grows,these computerised decision support systems(DSS)have great potential to help healthcare professionals improve diagnosis and care for individual patients.However,the implementation of these tools in clinical care raises some foreseeable legal challenges for healthcare providers and DSS-suppliers in Europe:How does the use of complex and novel DSS relate to professional standards to provide a reasonable standard of care?What should be done in terms of testing before DSS can be used in regular practice?What are the potential liabilities of health care providers and DSS companies if a DSS fails to function well?How do legal requirements for the protection of patient data and general privacy rights apply to likely DSS scenarios?In this article,we provide an overview of the current law and its general implications for the use of DSS,from a European perspective.We conclude that healthcare providers and DSS-suppliers will have the best chance of meeting legal challenges if:they are first tested in translational research with the patients’explicit,informed consent;DSS-suppliers and healthcare providers are able to clarify and agree on their individual legal responsibilities,and;patients are properly informed about privacy risks and able to decide themselves whether their data can be used for other purposes,or are stored and processed outside the EU.DSS developers and healthcare providers will need to work together closely to ensure compliance with national and European regulations and standards required for reasonable and safe patient care.Relevance to Patients:Advanced digital decision support systems have the potential to improve patient diagnosis and care.In this article we discuss key legal issues to support translational research using DSS and ensure that they meet the high standards for protection of patient safety and privacy in Europe. | Colin Mitchell Corrette Ploem | 2018 | Journal of Clinical & Translational Research2018，4，3： | 1 |
| 8 | Research on Big Data Application of Medical Health Management and Service显示文摘This paper discussed the background of medical information;summarizing the current situation of medical information,market analysis and construction process.The big data applications of medical health management and service in our daily life,including clinical decision-making,remote treatment,personalized medical care and etc.,were also discussed.Finally,the current challenges of medical big data were analyzed,and suggestions were proposed to improve the current situations. | Meng Pan Weiqing Xue Shaohui Xu | 2018 | Big Data Analytics for Healthcare2018，1，1： | 0 |
| 9 | SMARTER TREATMENT显示文摘Big data engineer Li Kun's routine work irv volves analyzing medical treatment data,once an area entirely unfamiliar to him.Majoring in computer science at the Chinese Academy of Sciences,Li has risen to his current position through his steady efforts.'I used to work on the research and development of database and data storage systems.Then I shifted to big data analysis,a job that is more intensive,and have been focusing on the medical treatment field ever since,'Li told Beijing Review.The engineer,in his 30s,has worked on databases for four years and spent three years on data analysis. | Li Xiaoyang | 2019 | Beijing Review2019，62，22： | 0 |
| 10 | An algorithm of linear modelling for biomedical data analysis显示文摘Analgorithmoflinearmodellingforbiomedicaldataanalysis￥XuYongyong(徐勇勇);CaoXiutang(曹秀堂);XiaJielai(夏结来)(DepartmentofHealthStatis... | 徐勇勇 曹秀堂 夏结来 | 1994 | Journal of Medical Colleges of PLA(China)1994，9，3： | 0 |
| 11 | Specific Data Mining Model of Massive Health Data显示文摘The mining of massive medicine data is one of the most widely problem in our world. However, it’s efficiency and accuracy are still not satisfactory. Many traditional mining algorithms which calculate repeatedly to reduce the dependence between data always ignore the correlation between them. To improve the effect of diagnosis, we extract some special features by conducting a preliminary classification and identification. Then, the specific characteristics of various medical data is mined by correlation mining method. The simulation experimental results demonstrate the validity of the improved algorithm. | Cuixia Li Shuyan Zhang Dingbiao Wang | 2016 | 国际计算机前沿大会会议论文集2016，，1： | 0 |
| 12 | Enhanced secure medical data sharing with traceable and direct revocation显示文摘Sharing of the electronic medical records among different hospitals raises serious concern of the leakage of individual privacy for the adoption of the semi trustworthiness of the medical cloud platform.The tracking and revocation of malicious users have become urgent problems.To solve these problems,this paper proposed a traceable and directly revocable medical data sharing scheme.In the scheme,a unique identity parameter(ID),which was generated and embedded in the private key generation phase by the medical service provider(MSP),is used to identify legal authorized user and trace malicious user.Only when attributes satisfy the access policy and user’s ID is not in the revocation list can the user calculate the decryption key.Malicious user can be tracked and directly revoked by using the revocation list.Under the assumption of decision bilinear Diffie-Hellman(DBDH),this paper has proved that the scheme is able to achieve security against chosen-plaintext attack(CPA).The performance analysis demonstrates that the sizes of the public key and private key are shorter,and the time overhead is less than other schemes in the public-private key generation,data encryption and data decryption stages. | Peng Weiping Cui Shuang Song Cheng Han Ning | 2023 | The Journal of China Universities of Posts and Telecommunications2023，30，1： | 0 |
| 13 | Design and Implementation of Medical Data Management System显示文摘At present,with the development of medical technology,the amount of medical data is increasing,in face of the shortcomings like the complexity of information,lack of structure,flexible changes,as well as the lack of scientific medical data management platforms at home and abroad,this paper presents a child epilepsy Data platform.This platform can extract quantitative data of structural epilepsy from the description of the complex and redundant text,which makes the data analysis and data mining more convenient and provides the basis for the future intelligent diagnosis,prediction and intelligent prognosis of epilepsy.At the same time,this paper introduces the platform database,server language and the construction of back-end and front-end of platform.The paper also introduces the technology and method of data-oriented management platform from requirement analysis,platform design to implementation stage.Through continuous testing and communication with doctors,developers finally established a data-driven system that is efficient in storing,managing and transferring and exporting medical data,which is also expandable,robust and stable. | Jie Wang Jianqiao Liu Jian Li Jian Zhang Qi Lei | 2017 | 国际计算机前沿大会会议论文集2017，，1： | 0 |
| 14 | Storage and Parallel Loading System Based on Mode Network for Multimode Medical Image Data显示文摘Since Multimode data is composed of many modes and their complex relationships,it cannot be retrieved or mined effectively by utilizing traditional analysis and processing techniques for single mode data.To address the challenges,we design and implement a graph-based storage and parallel loading system aimed at multimode medical image data.The system is a framework designed to flexibly store and rapidly load these multimode data.Specifically,the system utilizes the Mode Network to model the modes and their relationships in multimode medical image data and the graph database to store the data with a parallel loading technique. | Xiao Zhai Haiwei Pan Xiaoqin Xie Zhiqiang Zhang Qilong Han | 2016 | 国际计算机前沿大会会议论文集2016，，2： | 0 |
| 15 | Medical Data Mining: the pilot of medical data analysis in the era of big data显示文摘Clinical databases have accumulated large quantities of information about patients and their medical conditions. Current challenges in biomedical research and clinical practice include information overload and the need to optimize workflows, processes and guidelines, to increase capacity while reducing costs and improving efficiency. There is an urgent need for integrative and interactive machine learning solutions, because no medical doctor or biomedical researcher can keep pace today with the increasingly large and complex data sets – often called 'Big Data'. | Xiong-Zhi Wu | 2018 | Medical Data Mining2018，1，1： | 0 |
| 16 | Analysis on the Construction and Operation Mode of Internet Hospitals显示文摘Internet + medical health have become anational key strategy, and the industries of medicalinformation have ushered in new development.Internet hospitals have broken through the limitationsof diagnosis and treatment. Diagnosis, doctor’seducation, family doctors, and post-hospital trackinghave been introduced to the Internet hospital system,the whole process of medical management serviceincluding ‘health management, diagnosis, in-hospital,rehabilitation’ has been gradually established,and coordinating to the medical resources, familydoctors and channels of medicine, to realize the realcirculation of medical resources. The authors screenedsix Internet hospitals and comprehensively analyzedtheir construction and operation modes, with a viewto providing reference for the construction of Internethospitals. | Lianggang Nie Liangquan He Geng Lan Huimin He | 2019 | Proceedings of Business and Economic Studies2019，2，6： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员