scholarly journals Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

2019 ◽  
Vol 9 (11) ◽  
pp. 2331 ◽  
Author(s):  
Luis Bote-Curiel ◽  
Sergio Muñoz-Romero ◽  
Alicia Gerrero-Curieses ◽  
José Luis Rojo-Álvarez
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Science ◽  
Critical Discussion ◽  
Healthcare Sector ◽  
Simple Description ◽  
Future Evolution ◽  
Success Stories ◽  
Current State ◽  
Application Fields

In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

Big Data Analytics in Healthcare Sector

2019 ◽  
pp. 94-106 ◽  
Author(s):  
Sheik Abdullah A. ◽  
Selvakumar S. ◽  
Parkavi R. ◽  
Suganya R. ◽  
Abirami A. M.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Implementation Process ◽  
Healthcare Sector ◽  
Data Preparation ◽  
Healthcare Data ◽  
R Programming ◽  
Python Programming

The importance of big data over analytics made the process of solving various real-world problems simpler. The big data and data science tool box provided a realm of data preparation, data analysis, implementation process, and solutions. Data connections over any data source, data preparation for analysis has been made simple with the availability of tremendous tools in data analytics package. Some of the analytical tools include R programming, python programming, rapid analytics, and weka. The patterns and the granularity over the observed data can be fetched with the visualizations and data observations. This chapter provides an insight regarding the types of analytics in a big data perspective with the realm in applicability towards healthcare data. Also, the processing paradigms and techniques can be clearly observed through the chapter contents.

scholarly journals Biomedical informatics meets data science: current state and future directions for interaction

JAMIA Open ◽  
2018 ◽  
Vol 1 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Philip R O Payne ◽  
Elmer V Bernstam ◽  
Justin B Starren
Keyword(s):  
Big Data ◽  
Data Science ◽  
Clinical Decision Making ◽  
Social Issues ◽  
Biomedical Informatics ◽  
Scientific Discovery ◽  
Topic Area ◽  
Future Directions ◽  
Current State ◽  
Challenges And Opportunities

Abstract There are an ever-increasing number of reports and commentaries that describe the challenges and opportunities associated with the use of big data and data science (DS) in the context of biomedical education, research, and practice. These publications argue that there are substantial benefits resulting from the use of data-centric approaches to solve complex biomedical problems, including an acceleration in the rate of scientific discovery, improved clinical decision making, and the ability to promote healthy behaviors at a population level. In addition, there is an aligned and emerging body of literature that describes the ethical, legal, and social issues that must be addressed to responsibly use big data in such contexts. At the same time, there has been growing recognition that the challenges and opportunities being attributed to the expansion in DS often parallel those experienced by the biomedical informatics community. Indeed, many informaticians would consider some of these issues relevant to the core theories and methods incumbent to the field of biomedical informatics science and practice. In response to this topic area, during the 2016 American College of Medical Informatics Winter Symposium, a series of presentations and focus group discussions intended to define the current state and identify future directions for interaction and collaboration between people who identify themselves as working on big data, DS, and biomedical informatics were conducted. We provide a perspective concerning these discussions and the outcomes of that meeting, and also present a set of recommendations that we have generated in response to a thematic analysis of those same outcomes. Ultimately, this report is intended to: (1) summarize the key issues currently being discussed by the biomedical informatics community as it seeks to better understand how to constructively interact with the emerging biomedical big data and DS fields; and (2) propose a framework and agenda that can serve to advance this type of constructive interaction, with mutual benefit accruing to both fields.

scholarly journals Big Data is Too Small: Research Implications of Class Inequality for Online Data Collection

2018 ◽  
Author(s):  
Jen Schradie
Keyword(s):  
Big Data ◽  
Data Science ◽  
Digital Data ◽  
The Internet ◽  
Sociological Research ◽  
Marginalized Populations ◽  
Online Data ◽  
Persistent Problem ◽  
Current State ◽  
Using Data

With a growing interest in data science and online analytics, researchers are increasingly using data derived from the Internet. Whether for qualitative or quantitative analysis, online data, including “Big Data,” can often exclude marginalized populations, especially those from the poor and working class, as the digital divide remains a persistent problem. This methodological commentary on the current state of digital data and methods disentangles the hype from the reality of digitally produced data for sociological research. In the process, it offers strategies to address the weaknesses of data that is derived from the Internet in order to represent marginalized populations.

scholarly journals Urban Hydroinformatics: Past, Present and Future

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1959 ◽  
Author(s):  
Makropoulos ◽  
Savić
Keyword(s):  
Data Science ◽  
Continuous Process ◽  
Shared Vision ◽  
Water Sector ◽  
Science Data ◽  
Digital Revolution ◽  
Future Evolution ◽  
Research And Innovation ◽  
Current State ◽  
State Of Art

Hydroinformatics, as an interdisciplinary domain that blurs boundaries between water science, data science and computer science, is constantly evolving and reinventing itself. At the heart of this evolution, lies a continuous process of critical (self) appraisal of the discipline’s past, present and potential for further evolution, that creates a positive feedback loop between legacy, reality and aspirations. The power of this process is attested by the successful story of hydroinformatics thus far, which has arguably been able to mobilize wide ranging research and development and get the water sector more in tune with the digital revolution of the past 30 years. In this context, this paper attempts to trace the evolution of the discipline, from its computational hydraulics origins to its present focus on the complete socio-technical system, by providing at the same time, a functional framework to improve the understanding and highlight the links between different strands of the state-of-art hydroinformatic research and innovation. Building on this state-of-art landscape, the paper then attempts to provide an overview of key developments that are coming up, on the discipline’s horizon, focusing on developments relevant to urban water management, while at the same time, highlighting important legal, ethical and technical challenges that need to be addressed to ensure that the brightest aspects of this potential future are realized. Despite obvious limitations imposed by a single paper’s ability to report on such a diverse and dynamic field, it is hoped that this work contributes to a better understanding of both the current state of hydroinformatics and to a shared vision on the most exciting prospects for the future evolution of the discipline and the water sector it serves.

scholarly journals Gradient Boosting Machine and Deep Learning Approach in Big Data Analysis

2022 ◽  
Vol 15 (1) ◽  
pp. 1-20
Author(s):  
Ravinder Kumar ◽  
Lokesh Kumar Shrivastav
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Processing ◽  
High Frequency ◽  
Data Science ◽  
Processing System ◽  
High Frequency Data ◽  
Gradient Boosting ◽  
Frequency Data ◽  
Gradient Boosting Machine

Designing a system for analytics of high-frequency data (Big data) is a very challenging and crucial task in data science. Big data analytics involves the development of an efficient machine learning algorithm and big data processing techniques or frameworks. Today, the development of the data processing system is in high demand for processing high-frequency data in a very efficient manner. This paper proposes the processing and analytics of stochastic high-frequency stock market data using a modified version of suitable Gradient Boosting Machine (GBM). The experimental results obtained are compared with deep learning and Auto-Regressive Integrated Moving Average (ARIMA) methods. The results obtained using modified GBM achieves the highest accuracy (R2 = 0.98) and minimum error (RMSE = 0.85) as compared to the other two approaches.

Advanced Threat Detection Based on Big Data Technologies

2018 ◽  
pp. 1-19
Author(s):  
Madhvaraj M. Shetty ◽  
Manjaiah D. H.
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Threat Detection ◽  
Cyber Threats ◽  
Threat Intelligence ◽  
Big Data Technologies

Today constant increase in number of cyber threats apparently shows that current countermeasures are not enough to defend it. With the help of huge generated data, big data brings transformative potential for various sectors. While many are using it for better operations, some of them are noticing that it can also be used for security by providing broader view of vulnerabilities and risks. Meanwhile, deep learning is coming up as a key role by providing predictive analytics solutions. Deep learning and big data analytics are becoming two high-focus of data science. Threat intelligence becoming more and more effective. Since it is based on how much data collected about active threats, this reason has taken many independent vendors into partnerships. In this chapter, we explore big data and big data analytics with its benefits. And we provide a brief overview of deep analytics and finally we present collaborative threat Detection. We also investigate some aspects of standards and key functions of it. We conclude by presenting benefits and challenges of collaborative threat detection.

Advanced Threat Detection Based on Big Data Technologies

2020 ◽  
pp. 808-822
Author(s):  
Madhvaraj M. Shetty ◽  
Manjaiah D. H.
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Threat Detection ◽  
Cyber Threats ◽  
Threat Intelligence ◽  
Big Data Technologies

Today constant increase in number of cyber threats apparently shows that current countermeasures are not enough to defend it. With the help of huge generated data, big data brings transformative potential for various sectors. While many are using it for better operations, some of them are noticing that it can also be used for security by providing broader view of vulnerabilities and risks. Meanwhile, deep learning is coming up as a key role by providing predictive analytics solutions. Deep learning and big data analytics are becoming two high-focus of data science. Threat intelligence becoming more and more effective. Since it is based on how much data collected about active threats, this reason has taken many independent vendors into partnerships. In this chapter, we explore big data and big data analytics with its benefits. And we provide a brief overview of deep analytics and finally we present collaborative threat Detection. We also investigate some aspects of standards and key functions of it. We conclude by presenting benefits and challenges of collaborative threat detection.

Standardizing Physiologic Assessment Data to Enable Big Data Analytics

2016 ◽  
Vol 39 (1) ◽  
pp. 63-77 ◽  
Author(s):  
Susan A. Matney ◽  
Theresa (Tess) Settergren ◽  
Jane M. Carrington ◽  
Rachel L. Richesson ◽  
Amy Sheide ◽  
...  
Keyword(s):  
Big Data ◽  
Data Science ◽  
Big Data Analytics ◽  
Snomed Ct ◽  
Assessment Data ◽  
Nursing Assessment ◽  
Coding Systems ◽  
Current State ◽  
Data Elements ◽  
Systematized Nomenclature Of Medicine

Disparate data must be represented in a common format to enable comparison across multiple institutions and facilitate Big Data science. Nursing assessments represent a rich source of information. However, a lack of agreement regarding essential concepts and standardized terminology prevent their use for Big Data science in the current state. The purpose of this study was to align a minimum set of physiological nursing assessment data elements with national standardized coding systems. Six institutions shared their 100 most common electronic health record nursing assessment data elements. From these, a set of distinct elements was mapped to nationally recognized Logical Observations Identifiers Names and Codes (LOINC®) and Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT®) standards. We identified 137 observation names (55% new to LOINC), and 348 observation values (20% new to SNOMED CT) organized into 16 panels (72% new LOINC). This reference set can support the exchange of nursing information, facilitate multi-site research, and provide a framework for nursing data analysis.

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