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.

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.

Diversions: How the Underrepresentation of Research on Advantaged Groups Leaves Explanations for Health Inequalities Incomplete

2021 ◽  
pp. 002214652110281
Author(s):  
Bruce G. Link ◽  
San Juanita García
Keyword(s):  
Health Inequalities ◽  
Research Process ◽  
Data Sets ◽  
Sociological Research ◽  
Policy Recommendations ◽  
Major Health ◽  
Related Data ◽  
Health Related ◽  
Disadvantaged People

We identify a gap in health inequalities research that sociologists are particularly well situated to fill—an underrepresentation of research on the role advantaged groups play in creating inequalities. We name the process that creates the imbalance health-inequality diversions. We gathered evidence from awarded grants (349), major health-related data sets (7), research articles (324), and Healthy People policy recommendations. We assess whether the inequality-generating actions of advantaged groups are considered either directly by capturing their behaviors or indirectly by asking disadvantaged people about discrimination or exploitation from advantaged groups. We further assess whether there is a tendency to locate the problem in the person or group experiencing health inequalities. We find that diversions are prevalent across all steps of the research process. The diversion concept suggests new lines of sociological research to understand why diversions occur and how gaps in evidence concerning the role of the advantaged might be filled.

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 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.

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.

Big Data Analytics Using Apache Hive to Analyze Health Data

2019 ◽  
pp. 358-372
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.

scholarly journals Negotiating the reuse of health-data: Research, Big Data, and the European General Data Protection Regulation

2019 ◽  
Vol 6 (2) ◽  
pp. 205395171986259 ◽  
Author(s):  
Johannes Starkbaum ◽  
Ulrike Felt
Keyword(s):  
Big Data ◽  
Data Protection ◽  
Data Science ◽  
Data Reuse ◽  
Data Governance ◽  
General Data Protection Regulation ◽  
Related Data ◽  
Sustainable Implementation ◽  
Health Related ◽  
General Data

Before the EU General Data Protection Regulation entered into force in May 2018, we witnessed an intense struggle of actors associated with data-dependent fields of science, in particular health-related academia and biobanks striving for legal derogations for data reuse in research. These actors engaged in a similar line of argument and formed issue alliances to pool their collective power. Using descriptive coding followed by an interpretive analysis, this article investigates the argumentative repertoire of these actors and embeds the analysis in ethical debates on data sharing and biobank-related data governance. We observe efforts to perform a paradigmatic shift of the discourse around the General Data Protection Regulation-implementation away from ‘protecting data’ as key concern to ‘protecting health’ of individuals and societies at large. Instead of data protection, the key risks stressed by health researchers became potential obstacles to research. In line, exchange of information with data subjects is not a key concern in the arguments of biobank-related actors and it is assumed that patients want ‘their’ data to be used. We interpret these narratives as a ‘reaction’ to potential restrictions for data reuse and in line with a broader trend towards Big Data science, as the very idea of biobanking is conceptualized around long-term use of readily prepared data. We conclude that a sustainable implementation of biobanks needs not only to comply with the General Data Protection Regulation, but must proactively re-imagine its relation to citizens and data subjects in order to account for the various ways that science gets entangled with society.

Influences on User Trust in Healthcare Artificial Intelligence (HAI): A Systematic Review (Preprint)

2021 ◽  
Author(s):  
Eva Jermutus ◽  
Dylan Kneale ◽  
James Thomas ◽  
Susan Michie
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Data Privacy ◽  
Publication Date ◽  
Related Data ◽  
Healthcare Data ◽  
Healthcare It ◽  
Starting Point ◽  
Health Related ◽  
User Trust

BACKGROUND Artificial Intelligence (AI) is becoming increasingly prominent in domains such as healthcare. It is argued to be transformative through altering the way in which healthcare data is used as well as tackling rising costs and staff shortages. The realisation and success of AI depends heavily on people’s trust in its applications. Yet, the influences on trust in AI applications in healthcare so far have been underexplored OBJECTIVE The objective of this study was to identify aspects (related to users, the AI application and the wider context) influencing trust in healthcare AI (HAI). METHODS We performed a systematic review to map out influences on user trust in HAI. To identify relevant studies, we searched 7 electronic databases in November 2019 (ACM digital library, IEEE Explore, NHS Evidence, Ovid ProQuest Dissertations & Thesis Global, Ovid PsycINFO, PubMed, Web of Science Core Collection). Searches were restricted to publications available in English and German with no publication date restriction. To be included studies had to be empirical; focus on an AI application (excluding robotics) in a health-related setting; and evaluate applications with regards to users. RESULTS Overall, 3 studies, one mixed-method and 2 qualitative studies in English were included. Influences on trust fell into three broad categories: human-related (knowledge, expectation, mental model, self-efficacy, type of user, age, gender), AI-related (data privacy and safety, operational safety, transparency, design, customizability, trialability, explainability, understandability, power-control-balance, benevolence) and related to wider context (AI company, media, social network of the user). The factors resulted in an updated logic model illustrating the relationship between these aspects. CONCLUSIONS Trust in healthcare AI depends on a variety of factors, both external and internal to the AI application. This study contributes to our understanding of what influences trust in HAI by highlighting key influences as well as pointing to gaps and issues in existing research on trust and AI. In so doing, it offers a starting point for further investigation of trust environments as well as trustworthy AI applications.

scholarly journals EHR Big Data Deep Phenotyping

2014 ◽  
Vol 23 (01) ◽  
pp. 206-211 ◽  
Author(s):  
L. Lenert ◽  
G. Lopez-Campos ◽  
L. J. Frey
Keyword(s):  
Big Data ◽  
Medical Records ◽  
Clinical Findings ◽  
Software Systems ◽  
Related Data ◽  
Processing Power ◽  
Health Related ◽  
Hardware Processing ◽  
Definition Of ◽  
Deep Phenotyping

Summary Objectives: Given the quickening speed of discovery of variant disease drivers from combined patient genotype and phenotype data, the objective is to provide methodology using big data technology to support the definition of deep phenotypes in medical records. Methods: As the vast stores of genomic information increase with next generation sequencing, the importance of deep phenotyping increases. The growth of genomic data and adoption of Electronic Health Records (EHR) in medicine provides a unique opportunity to integrate phenotype and genotype data into medical records. The method by which collections of clinical findings and other health related data are leveraged to form meaningful phenotypes is an active area of research. Longitudinal data stored in EHRs provide a wealth of information that can be used to construct phenotypes of patients. We focus on a practical problem around data integration for deep phenotype identification within EHR data. The use of big data approaches are described that enable scalable markup of EHR events that can be used for semantic and temporal similarity analysis to support the identification of phenotype and genotype relationships. Conclusions: Stead and colleagues’ 2005 concept of using light standards to increase the productivity of software systems by riding on the wave of hardware/processing power is described as a harbinger for designing future healthcare systems. The big data solution, using flexible markup, provides a route to improved utilization of processing power for organizing patient records in genotype and phenotype research.

Sign in / Sign up

Export Citation Format

Share Document