scholarly journals Big Data, precision medicine and private insurance: A delicate balancing act

2019 ◽  
Vol 6 (1) ◽  
pp. 205395171983011 ◽  
Author(s):  
Alessandro Blasimme ◽  
Effy Vayena ◽  
Ine Van Hoyweghen
Keyword(s):  
Big Data ◽  
Precision Medicine ◽  
Insurance Industry ◽  
Private Insurance ◽  
Economic Value ◽  
Medical Science ◽  
Personal Data ◽  
Phenotypic Data ◽  
Related Data ◽  
Health Related

In this paper, we discuss how access to health-related data by private insurers, other than affecting the interests of prospective policy-holders, can also influence their propensity to make personal data available for research purposes. We take the case of national precision medicine initiatives as an illustrative example of this possible tendency. Precision medicine pools together unprecedented amounts of genetic as well as phenotypic data. The possibility that private insurers could claim access to such rapidly accumulating biomedical Big Data or to health-related information derived from it would discourage people from enrolling in precision medicine studies. Should that be the case, the economic value of personal data for the insurance industry would end up affecting the public value of data as a scientific resource. In what follows we articulate three principles – trustworthiness, openness and evidence – to address this problem and tame its potentially harmful effects on the development of precision medicine and, more generally, on the advancement of medical science.

scholarly journals Big Data Analytics & Artificial Intelligence in Healthcare

2021 ◽  
Author(s):  
PRANJAL KUMAR ◽  
Siddhartha Chauhan
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Large Scale ◽  
Health Sector ◽  
Big Data Analytics ◽  
Personal Data ◽  
Related Data ◽  
Large Scale Data ◽  
Ethical Policy ◽  
Health Related

Abstract Big data analysis and Artificial Intelligence have received significant attention recently in creating more opportunities in the health sector for aggregating or collecting large-scale data. Today, our genomes and microbiomes can be sequenced i.e., all information exchanged between physicians and patients in Electronic Health Records (EHR) can be collected and traced at least theoretically. Social media and mobile devices today obviously provide many health-related data regarding activity, diets, social contacts, and so on. However, it is increasingly difficult to use this information to answer health questions and, in particular, because the data comes from various domains and lives in different infrastructures and of course it also is very variable quality. The massive collection and aggregation of personal data come with a number of ethical policy, methodological, technological challenges. It should be acknowledged that large-scale clinical evidence remains to confirm the promise of Big Data and Artificial Intelligence (AI) in health care. This paper explores the complexities of big data & artificial intelligence in healthcare as well as the benefits and prospects.

scholarly journals The funhouse mirror: the I in personalised healthcare

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Mira W. Vegter ◽  
Hub A. E. Zwart ◽  
Alain J. van Gool
Keyword(s):  
Precision Medicine ◽  
Personal Data ◽  
Health Data ◽  
Personal Health ◽  
Lifestyle Interventions ◽  
Phenotypic Data ◽  
Individual Health ◽  
Data Source ◽  
Tracking Devices ◽  
Personalised Healthcare

AbstractPrecision Medicine is driven by the idea that the rapidly increasing range of relatively cheap and efficient self-tracking devices make it feasible to collect multiple kinds of phenotypic data. Advocates of N = 1 research emphasize the countless opportunities personal data provide for optimizing individual health. At the same time, using biomarker data for lifestyle interventions has shown to entail complex challenges. In this paper, we argue that researchers in the field of precision medicine need to address the performative dimension of collecting data. We propose the fun-house mirror as a metaphor for the use of personal health data; each health data source yields a particular type of image that can be regarded as a ‘data mirror’ that is by definition specific and skewed. This requires competence on the part of individuals to adequately interpret the images thus provided.

scholarly journals The forthcoming era of precision medicine

2016 ◽  
Vol 45 (2) ◽  
pp. 144
Author(s):  
Stjepan Gamulin
Keyword(s):  
Health Care ◽  
Medical Education ◽  
Big Data ◽  
Precision Medicine ◽  
Personal Data ◽  
Legislative Framework ◽  
Regulatory Bodies ◽  
Tailored Approach ◽  
Biomedical Community ◽  
Prevention And Therapy

<p><strong>Abstract</strong>. The aim of this essay is to present the definition and principles of personalized or precision medicine, the perspective and barriers to its development and clinical application. The implementation of precision medicine in health care requires the coordinated efforts of all health care stakeholders (the biomedical community, government, regulatory bodies, patients’ groups). Particularly, translational research with the integration of genomic and comprehensive data from all levels of the organism (“big data”), development of bioinformatics platforms enabling network analysis of disease etiopathogenesis, development of a legislative framework for handling personal data, and new paradigms of medical education are necessary for successful application of the concept of precision medicine in health care. <strong>Conclusion</strong>. In the present and future era of precision medicine, the collaboration of all participants in health care is necessary for its realization, resulting in improvement of diagnosis, prevention and therapy, based on a holistic, individually tailored approach.</p>

Deep Learning in Early Detection of Alzheimer's

2019 ◽  
pp. 153-162
Author(s):  
Anitha S. Pillai ◽  
Bindu Menon
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Early Detection ◽  
Big Data Analytics ◽  
Vital Role ◽  
Smart Devices ◽  
Brain Images ◽  
Related Data ◽  
Computer Scientists ◽  
Health Related

Advancement in technology has paved the way for the growth of big data. We are able to exploit this data to a great extent as the costs of collecting, storing, and analyzing a large volume of data have plummeted considerably. There is an exponential increase in the amount of health-related data being generated by smart devices. Requisite for proper mining of the data for knowledge discovery and therapeutic product development is very essential. The expanding field of big data analytics is playing a vital role in healthcare practices and research. A large number of people are being affected by Alzheimer's Disease (AD), and as a result, it becomes very challenging for the family members to handle these individuals. The objective of this chapter is to highlight how deep learning can be used for the early diagnosis of AD and present the outcomes of research studies of both neurologists and computer scientists. The chapter gives introduction to big data, deep learning, AD, biomarkers, and brain images and concludes by suggesting blood biomarker as an ideal solution for early detection of AD.

scholarly journals Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

2020 ◽  
Vol 9 (4) ◽  
pp. 1107 ◽  
Author(s):  
Charat Thongprayoon ◽  
Wisit Kaewput ◽  
Karthik Kovvuru ◽  
Panupong Hansrivijit ◽  
Swetha R. Kanduri ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Kidney Diseases ◽  
Major Health ◽  
Related Data ◽  
Form Part ◽  
Health Related ◽  
Health Burdens ◽  
Excellent Tool ◽  
Advance Knowledge

Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.

How GDPR Enhances Transparency and Fosters Pseudonymisation in Academic Medical Research

2020 ◽  
Vol 27 (1) ◽  
pp. 35-57
Author(s):  
G. Verhenneman ◽  
K. Claes ◽  
J.J. Derèze ◽  
P. Herijgers ◽  
C. Mathieu ◽  
...  
Keyword(s):  
Medical Research ◽  
Academic Research ◽  
Code Of Conduct ◽  
Personal Data ◽  
University Hospitals ◽  
General Data Protection Regulation ◽  
Related Data ◽  
Academic Medical ◽  
Health Related ◽  
Research Organisations

Abstract The European General Data Protection Regulation (GDPR) has dotted the i’s and crossed the t’s in the context of academic medical research. One year into GDPR, it is clear that a change of mind and the uptake of new procedures is required. Research organisations have been looking at the possibility to establish a code-of-conduct, good practices and/or guidelines for researchers that translate GDPR’s abstract principles to concrete measures suitable for implementation. We introduce a proposal for the implementation of GDPR in the context of academic research which involves the processing of health related data, as developed by a multidisciplinary team at the University Hospitals Leuven. The proposal is based on three elements, three stages and six specific safeguards. Transparency and pseudonymisation are considered key to find a balance between the need for researchers to collect and analyse personal data and the increasing wish of data subjects for informational control.

Biobanks as a Central Part of the Finnish Growth and Genomic Strategies: How to Balance Privacy in an Innovation Ecosystem?

2016 ◽  
Vol 44 (1) ◽  
pp. 24-34 ◽  
Author(s):  
Sirpa Soini
Keyword(s):  
National Level ◽  
Personal Data ◽  
Genomic Research ◽  
Research Setting ◽  
Innovation Ecosystem ◽  
Related Data ◽  
Biobank Research ◽  
Health Related ◽  
Identifiable Data ◽  
Broad Understanding

Finland has aimed to make itself an international leader in genomic research and related business and, in working towards that goal, has enacted biobank legislation. The Biobank Act requires biobanks to gain approval, be supervised, and register at the national level. Numerous other laws may also apply in any given research setting, such as the Personal Data Act, the Medical Research Act, and the Act on Medical Use of Human Organs and Tissues. In terms of privacy protection, anonymization is generally not permitted under Finnish law and therefore most biobanks pseudonomize data and samples. However, the broad understanding of what is identifiable data in Finland has created difficulties in sharing with non-EU countries. Furthermore, consent to biobank research is only applicable to the sample and related data, not to data stored in other health-related registries, and consent is only to the field of research for that particular biobank. These restrictions impede the sharing of samples and data for research.

scholarly journals The Interplay Between the GDPR and the IDD

2020 ◽  
pp. 255-292
Author(s):  
Viktoria Chatzara
Keyword(s):  
Data Protection ◽  
Insurance Industry ◽  
Personal Data ◽  
General Data Protection Regulation ◽  
Related Data ◽  
Adoption And Implementation ◽  
Key Issues ◽  
General Data ◽  
Distribution Laws ◽  
The Eu

AbstractThe General Data Protection Regulation (GDPR) and the Insurance Distribution Directive (IDD) have radically transformed the EU data protection and insurance distribution laws, thus constituting the two main regulatory sources of disruption for the insurance industry. The new IDD obligations require the adoption and implementation of compliance measures, which affect both the internal and the external operations of distributors, and which in numerous cases involve and even require the collection and processing of personal data in order to be effective and achieve the intended goals. As such, compliance with the IDD provisions needs to be designed in a way that respects the applicable GDPR provisions and ensures abidance by the related data protection obligations. This chapter aims to highlight some characteristic examples of areas where the IDD obligations mingle with the GDPR provisions, both in terms of the internal organization and functioning of insurers and intermediaries (Sect. 2), as well as with regard to the relations between distributors and their customers, and between distributors themselves (Sect. 3), and to pose some of the key issues that should be taken into account when attempting to tackle the interplay of these two sets of rules.

Big Data Analytics Using Apache Hive to Analyze Health Data

2022 ◽  
pp. 979-992
Author(s):  
Pavani Konagala
Keyword(s):  
Big Data ◽  
Stock Exchange ◽  
Big Data Analytics ◽  
Large Data ◽  
Massive Data ◽  
Data Sets ◽  
Related Data ◽  
Health Related ◽  
Relational Database Management ◽  
Apache Hive

A large volume of data is stored electronically. It is very difficult to measure the total volume of that data. This large amount of data is coming from various sources such as stock exchange, which may generate terabytes of data every day, Facebook, which may take about one petabyte of storage, and internet archives, which may store up to two petabytes of data, etc. So, it is very difficult to manage that data using relational database management systems. With the massive data, reading and writing from and into the drive takes more time. So, the storage and analysis of this massive data has become a big problem. Big data gives the solution for these problems. It specifies the methods to store and analyze the large data sets. This chapter specifies a brief study of big data techniques to analyze these types of data. It includes a wide study of Hadoop characteristics, Hadoop architecture, advantages of big data and big data eco system. Further, this chapter includes a comprehensive study of Apache Hive for executing health-related data and deaths data of U.S. government.

Deep Learning in Early Detection of Alzheimer's

2021 ◽  
pp. 160-169
Author(s):  
Anitha S. Pillai ◽  
Bindu Menon
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Early Detection ◽  
Big Data Analytics ◽  
Vital Role ◽  
Smart Devices ◽  
Brain Images ◽  
Related Data ◽  
Computer Scientists ◽  
Health Related

Advancement in technology has paved the way for the growth of big data. We are able to exploit this data to a great extent as the costs of collecting, storing, and analyzing a large volume of data have plummeted considerably. There is an exponential increase in the amount of health-related data being generated by smart devices. Requisite for proper mining of the data for knowledge discovery and therapeutic product development is very essential. The expanding field of big data analytics is playing a vital role in healthcare practices and research. A large number of people are being affected by Alzheimer's Disease (AD), and as a result, it becomes very challenging for the family members to handle these individuals. The objective of this chapter is to highlight how deep learning can be used for the early diagnosis of AD and present the outcomes of research studies of both neurologists and computer scientists. The chapter gives introduction to big data, deep learning, AD, biomarkers, and brain images and concludes by suggesting blood biomarker as an ideal solution for early detection of AD.

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