scholarly journals Technical Blossom in Medical Care: The Influence of Big Data Platform on Medical Innovation

2020 ◽  
Vol 17 (2) ◽  
pp. 516 ◽  
Author(s):  
Bai Liu ◽  
Shuyan Guo ◽  
Bin Ding
Keyword(s):  
Public Health ◽  
Big Data ◽  
Public Health Research ◽  
Medical Innovation ◽  
Promoting Effect ◽  
Exogenous Shock ◽  
Listed Firms ◽  
Data Platform ◽  
Medical Big Data ◽  
Medical Policy

Medical innovation has consistently been an essential subject and a source of support for public health research. Furthermore, improving the level of medical research and development is of great concern in this field. This paper highlights the role of big data in public medical innovation. Based on a sample of China’s listed firms in the medical industry from 2013 to 2018, this paper explores the exogenous shock effect of China’s big data medical policy. Results show that the construction of the medical big data platform effectively promotes innovation investment and the innovation patent of medical firms. In addition, the heterogeneity of this promoting effect is reflected in firm size through the overcoming of different innovation bottlenecks. The research conclusions support the positive significance of the macro-led implementation of the medical big data platform, and suggest that the positive economic externalities generated by this policy are critical to public health.

Navigating the Ethics of Big Data in Public Health

2019 ◽  
pp. 353-367
Author(s):  
Effy Vayena ◽  
Lawrence Madoff
Keyword(s):  
Public Health ◽  
Big Data ◽  
Credit Card ◽  
Health Sector ◽  
Public Health Research ◽  
Ethical Challenges ◽  
The Public ◽  
Health Community ◽  
Digital Disease Detection ◽  
The Public Sphere

“Big data,” which encompasses massive amounts of information from both within the health sector (such as electronic health records) and outside the health sector (social media, search queries, cell phone metadata, credit card expenditures), is increasingly envisioned as a rich source to inform public health research and practice. This chapter examines the enormous range of sources, the highly varied nature of these data, and the differing motivations for their collection, which together challenge the public health community in ethically mining and exploiting big data. Ethical challenges revolve around the blurring of three previously clearer boundaries: between personal health data and nonhealth data; between the private and the public sphere in the online world; and, finally, between the powers and responsibilities of state and nonstate actors in relation to big data. Considerations include the implications for privacy, control and sharing of data, fair distribution of benefits and burdens, civic empowerment, accountability, and digital disease detection.

scholarly journals Differential Diagnosis Model of Hypocellular Myelodysplastic Syndrome and Aplastic Anemia Based on the Medical Big Data Platform

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jianhui Wu ◽  
Lu Zhang ◽  
Sufeng Yin ◽  
Haidong Wang ◽  
Guoli Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Big Data ◽  
Differential Diagnosis ◽  
Decision Tree ◽  
Cell Morphology ◽  
Bp Neural Network ◽  
Data Platform ◽  
New Ideas ◽  
Medical Big Data

The arrival of the era of big data has brought new ideas to solve problems for all walks of life. Medical clinical data is collected and stored in the medical field by utilizing the medical big data platform. Based on medical information big data, new ideas and methods for the differential diagnosis of hypo-MDS and AA are studied. The basic information, peripheral blood classification counts, peripheral blood cell morphology, bone marrow cell morphology, and other information were collected from patients diagnosed with hypo-MDS and AA diagnosed in the first diagnosis. First, statistical analysis was performed. Then, the logistic regression model, decision tree model, BP neural network model, and support vector machine (SVM) model of hypo-MDS and AA were established. The sensitivity, specificity, Youden index, positive likelihood ratio (+LR), negative likelihood ratio (−LR), area under curve (AUC), accuracy, Kappa value, positive predictive value (+PV), negative predictive value (−PV) of the four model training set and test set were compared, respectively. Finally, with the support of medical big data, using logistic regression, decision tree, BP neural network, and SVM four classification algorithms, the decision tree algorithm is optimal for the classification of hypo-MDS and AA and analyzes the characteristics of the optimal model misjudgment data.

scholarly journals 13.C. Workshop: Epistemic and ethical innovations in public health research

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
◽  
Keyword(s):  
Public Health ◽  
Big Data ◽  
Health Research ◽  
Scientific Method ◽  
Ethical Issues ◽  
Public Health Research ◽  
Biomedical Data ◽  
Technical Problems ◽  
Ethical Problems ◽  
Public Health Policies

Abstract About 90% of the biomedical data accessible to researchers was created in the last two years. These data have a very meaningful impact both in creating public policies on health, as well as public health research. This certainly implies complex technical problems on how to store, analyse and distribute data, but it also brings relevant epistemological issues. In this workshop we will present some of such problems and discuss how epistemic innovation is key in order to tackle ethical issues related to the use of big data in public health research. Databases implied in public health research are so huge that they rise relevant questions about how scientific method is applied, such as what counts as evidence of a hypothesis when data can not be directly apprehended by humans, how to distinguish correlation from causation, or in which cases the provider of a database can be considered co-author of a research paper. To consider such issues nowadays, current protocols do not hold, and we need innovation in methodological and epistemic procedures. At the same time, due to the fact that a relevant deal of such biomedical data is linked to individual people, and how medical data can be used to predict and transform human behavior, there are ethical questions difficult to solve as they imply new challenges. Some of them are related to communication issues, so patients and citizens understand these new ethical problems that didn't arise before the development of big data; others relate to the way in which public health researchers can and can't store, analyse and distribute information, and some others relate to the limits on which technologies are ethically, safe and which ones bring erosion of basic human rights. The four contributions in the workshop analyse these questions in some detail. During the workshop we will present a coherent understanding on what is epistemic innovation, some logical tools necessary for its development, and then we will discuss several cases on how epistemic innovation applies to different aspects of public health research, also commenting its relevance when tackling ethical problems that may arise. Key messages The workshop deepens the ethical and epistemological innovations involved in public health policies and research, specially related to big data. The workshop analyses novel aspects of public health ethics

scholarly journals Research on the Secure Medical Big Data Ecosystem Based on Hadoop

2020 ◽  
Author(s):  
Xiangfeng Zhang ◽  
Yanmei Wang
Keyword(s):  
Information System ◽  
Big Data ◽  
Health Information System ◽  
Health Data ◽  
Personal Health ◽  
Medical Institutions ◽  
Data Platform ◽  
Data Ecosystem ◽  
Medical Big Data ◽  
Personalized Health

Abstract This paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. Since traditional healing activities take place in medical institutions, patient users must travel to these institutions to learn about their treatment status. The personalized health information system designed for this purpose enables patient users to understand their treatment and rehabilitation status anytime and anywhere. The above is a consideration from the perspective of the patient user, from the perspective of personal health data. Because traditional medical health data is scattered throughout different independent medical institutions, and these databases are heterogeneous. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. The characteristics of blockchain without a central server make the system without a single point In case of failure, the stability of the system is well maintained. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data is stored and analysed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health

2021 ◽  
Vol 43 (1) ◽  
Author(s):  
Paul Wesson ◽  
Yulin Hswen ◽  
Gilmer Valdez ◽  
Kristefer Stojanovski ◽  
Margaret A. Handley
Keyword(s):  
Public Health ◽  
Big Data ◽  
Data Augmentation ◽  
Public Health Research ◽  
Machine Learning Algorithms ◽  
Annual Review ◽  
Publication Date ◽  
Medical Claims ◽  
Marginalized Communities ◽  
Big Data Applications

The big data revolution presents an exciting frontier to expand public health research, broadening the scope of research and increasing the precision of answers. Despite these advances, scientists must be vigilant against also advancing potential harms toward marginalized communities. In this review, we provide examples in which big data applications have (unintentionally) perpetuated discriminatory practices, while also highlighting opportunities for big data applications to advance equity in public health. Here, big data is framed in the context of the five Vs (volume, velocity, veracity, variety, and value), and we propose a sixth V, virtuosity, which incorporates equity and justice frameworks. Analytic approaches to improving equity are presented using social computational big data, fairness in machine learning algorithms, medical claims data, and data augmentation as illustrations. Throughout, we emphasize the biasing influence of data absenteeism and positionality and conclude with recommendations for incorporating an equity lens into big data research. Expected final online publication date for the Annual Review of Public Health, Volume 43 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Research on intelligent medical big data system based on Hadoop and blockchain

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiangfeng Zhang ◽  
Yanmei Wang
Keyword(s):  
Information System ◽  
Big Data ◽  
Health Information ◽  
Health Information System ◽  
Health Data ◽  
Medical Rehabilitation ◽  
Data Platform ◽  
Data Ecosystem ◽  
Medical Big Data ◽  
Personalized Health

AbstractIn order to improve the intelligence of the medical system, this paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. In order to improve the efficiency of traditional medical rehabilitation activities and enable patients to maximize their understanding of their treatment status, this paper designs a personalized health information system that allows patient users to understand their treatment and rehabilitation status anytime and anywhere, and all medical health data distributed in different independent medical institutions to ensure that these data are stored independently. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data are stored and analyzed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

scholarly journals Research on intelligent medical big data system based on Hadoop and blockchain

2020 ◽  
Author(s):  
Xiangfeng Zhang ◽  
Yanmei Wang
Keyword(s):  
Information System ◽  
Big Data ◽  
Health Information ◽  
Health Information System ◽  
Health Data ◽  
Medical Rehabilitation ◽  
Data Platform ◽  
Data Ecosystem ◽  
Medical Big Data ◽  
Personalized Health

Abstract In order to improve the intelligence of the medical system, this paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. In order to improve the efficiency of traditional medical rehabilitation activities and enable patients to maximize their understanding of their treatment status, this paper designs a personalized health information system that allows patient users to understand their treatment and rehabilitation status anytime and anywhere, and all medical health data Distributed in different independent medical institutions to ensure that these data are stored independently. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data is stored and analysed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

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