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...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.展开更多
With the prevailing COVID-19 pandemic, the lack of digitally-recorded and connected health data poses a challenge for analysing the situation. Virus outbreaks, such as the current pandemic, allow for the optimisation ...With the prevailing COVID-19 pandemic, the lack of digitally-recorded and connected health data poses a challenge for analysing the situation. Virus outbreaks, such as the current pandemic, allow for the optimisation and reuse of data, which can be beneficial in managing future outbreaks. However, there is a general lack of knowledge about the actual flow of information in health facilities, which is also the case in Uganda. In Uganda, where this case study was conducted, there is no comprehensive knowledge about what type of data is collected or how it is collected along the journey of a patient through a health facility. This study investigates information flows of clinical patient data in health facilities in Uganda. The study found that almost all health facilities in Uganda store patient information in paper files on shelves. Hospitals in Uganda are provided with paper tools, such as reporting forms, registers and manuals, in which district data is collected as aggregate data and submitted in the form of digital reports to the Ministry of Health Resource Center. These reporting forms are not digitised and, thus, not machine-actionable. Hence, it is not easy for health facilities, researchers, and others to find and access patient and research data. It is also not easy to reuse and connect this data with other digital health data worldwide, leading to the incorrect conclusion that there is less health data in Uganda. The a FAIR architecture has the potential to solve such problems and facilitate the transition from paper to digital records in the Uganda health system.展开更多
The introduction of the electronic medical record(EHR)sharing system has made a great contribution to the management and sharing of healthcare data.Considering referral treatment for patients,the original signature ne...The introduction of the electronic medical record(EHR)sharing system has made a great contribution to the management and sharing of healthcare data.Considering referral treatment for patients,the original signature needs to be converted into a re-signature that can be verified by the new organization.Proxy re-signature(PRS)can be applied to this scenario so that authenticity and nonrepudiation can still be insured for data.Unfortunately,the existing PRS schemes cannot realize forward and backward security.Therefore,this paper proposes the first PRS scheme that can provide key-insulated property,which can guarantee both the forward and backward security of the key.Although the leakage of the private key occurs at a certain moment,the forward and backward key will not be attacked.Thus,the purpose of key insulation is implemented.What’s more,it can update different corresponding private keys in infinite time periods without changing the identity information of the user as the public key.Besides,the unforgeability of our scheme is proved based on the extended Computational Diffie-Hellman assumption in the random oracle model.Finally,the experimental simulation demonstrates that our scheme is feasible and in possession of promising properties.展开更多
区块链技术是一种新兴技术,它具备防篡改、去中心化、分布式存储等特点,可以有效地解决现有数据共享模型中隐私安全、用户控制权不足以及单点故障问题.本文以电子病历(Electronic health record,EHR)共享为例提出一种基于集成信用度评...区块链技术是一种新兴技术,它具备防篡改、去中心化、分布式存储等特点,可以有效地解决现有数据共享模型中隐私安全、用户控制权不足以及单点故障问题.本文以电子病历(Electronic health record,EHR)共享为例提出一种基于集成信用度评估智能合约的数据共享访问控制模型,为患者提供可信EHR共享环境和动态访问控制策略接口.实验表明所提模型有效解决了患者隐私安全和对EHR控制权不足的问题.同时就模型的特点、安全性以及性能进行了分析.展开更多
基金supported in part by the National Natural Science Foundation of China (Nos. 61772552 and 61772557)the 111 Project (No. B18059)the Hunan Provincial Science and Technology Program (No. 2018WK4001)
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.
基金VODAN-Africathe Philips Foundation+2 种基金the Dutch Development Bank FMOCORDAIDthe GO FAIR Foundation for supporting this research
文摘With the prevailing COVID-19 pandemic, the lack of digitally-recorded and connected health data poses a challenge for analysing the situation. Virus outbreaks, such as the current pandemic, allow for the optimisation and reuse of data, which can be beneficial in managing future outbreaks. However, there is a general lack of knowledge about the actual flow of information in health facilities, which is also the case in Uganda. In Uganda, where this case study was conducted, there is no comprehensive knowledge about what type of data is collected or how it is collected along the journey of a patient through a health facility. This study investigates information flows of clinical patient data in health facilities in Uganda. The study found that almost all health facilities in Uganda store patient information in paper files on shelves. Hospitals in Uganda are provided with paper tools, such as reporting forms, registers and manuals, in which district data is collected as aggregate data and submitted in the form of digital reports to the Ministry of Health Resource Center. These reporting forms are not digitised and, thus, not machine-actionable. Hence, it is not easy for health facilities, researchers, and others to find and access patient and research data. It is also not easy to reuse and connect this data with other digital health data worldwide, leading to the incorrect conclusion that there is less health data in Uganda. The a FAIR architecture has the potential to solve such problems and facilitate the transition from paper to digital records in the Uganda health system.
基金supported by the Network and Data Security Key Laboratory of Sichuan Province under the Grant No.NDS2021-2in part by Science and Technology Project of Educational Commission of Jiangxi Province under the Grant No.GJJ190464in part by National Natural Science Foundation of China under the Grant No.71661012.
文摘The introduction of the electronic medical record(EHR)sharing system has made a great contribution to the management and sharing of healthcare data.Considering referral treatment for patients,the original signature needs to be converted into a re-signature that can be verified by the new organization.Proxy re-signature(PRS)can be applied to this scenario so that authenticity and nonrepudiation can still be insured for data.Unfortunately,the existing PRS schemes cannot realize forward and backward security.Therefore,this paper proposes the first PRS scheme that can provide key-insulated property,which can guarantee both the forward and backward security of the key.Although the leakage of the private key occurs at a certain moment,the forward and backward key will not be attacked.Thus,the purpose of key insulation is implemented.What’s more,it can update different corresponding private keys in infinite time periods without changing the identity information of the user as the public key.Besides,the unforgeability of our scheme is proved based on the extended Computational Diffie-Hellman assumption in the random oracle model.Finally,the experimental simulation demonstrates that our scheme is feasible and in possession of promising properties.
文摘区块链技术是一种新兴技术,它具备防篡改、去中心化、分布式存储等特点,可以有效地解决现有数据共享模型中隐私安全、用户控制权不足以及单点故障问题.本文以电子病历(Electronic health record,EHR)共享为例提出一种基于集成信用度评估智能合约的数据共享访问控制模型,为患者提供可信EHR共享环境和动态访问控制策略接口.实验表明所提模型有效解决了患者隐私安全和对EHR控制权不足的问题.同时就模型的特点、安全性以及性能进行了分析.
