scholarly journals Clinical Research Informatics for Big Data and Precision Medicine

2016 ◽  
Vol 25 (01) ◽  
pp. 211-218 ◽  
Author(s):  
M.G. Kahn ◽  
C. Weng
Keyword(s):  
Big Data ◽  
Clinical Research ◽  
Rapid Development ◽  
Clinical Care ◽  
Big Data Analytics ◽  
Data Access ◽  
Fusion Data ◽  
New Concepts ◽  
Clinical Research Informatics ◽  
Near Term

Summary Objectives: To reflect on the notable events and significant developments in Clinical Research Informatics (CRI) in the year of 2015 and discuss near-term trends impacting CRI. Methods: We selected key publications that highlight not only important recent advances in CRI but also notable events likely to have significant impact on CRI activities over the next few years or longer, and consulted the discussions in relevant scientific communities and an online living textbook for modern clinical trials. We also related the new concepts with old problems to improve the continuity of CRI research. Results: The highlights in CRI in 2015 include the growing adoption of electronic health records (EHR), the rapid development of regional, national, and global clinical data research networks for using EHR data to integrate scalable clinical research with clinical care and generate robust medical evidence. Data quality, integration, and fusion, data access by researchers, study transparency, results reproducibility, and infrastructure sustainability are persistent challenges. Conclusion: The advances in Big Data Analytics and Internet technologies together with the engagement of citizens in sciences are shaping the global clinical research enterprise, which is getting more open and increasingly stakeholder-centered, where stakeholders include patients, clinicians, researchers, and sponsors.

scholarly journals Light-Weight, Self-Powered Sensor Based on Triboelectric Nanogenerator for Big Data Analytics in Sports

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2322
Author(s):  
Xiaofei Ma ◽  
Xuan Liu ◽  
Xinxing Li ◽  
Yunfei Ma
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Analytics ◽  
Mechanical Energy ◽  
Rapid Development ◽  
Big Data Analytics ◽  
Triboelectric Nanogenerator ◽  
Light Weight ◽  
Table Tennis ◽  
Self Powered

With the rapid development of the Internet of Things (IoTs), big data analytics has been widely used in the sport field. In this paper, a light-weight, self-powered sensor based on a triboelectric nanogenerator for big data analytics in sports has been demonstrated. The weight of each sensing unit is ~0.4 g. The friction material consists of polyaniline (PANI) and polytetrafluoroethylene (PTFE). Based on the triboelectric nanogenerator (TENG), the device can convert small amounts of mechanical energy into the electrical signal, which contains information about the hitting position and hitting velocity of table tennis balls. By collecting data from daily table tennis training in real time, the personalized training program can be adjusted. A practical application has been exhibited for collecting table tennis information in real time and, according to these data, coaches can develop personalized training for an amateur to enhance the ability of hand control, which can improve their table tennis skills. This work opens up a new direction in intelligent athletic facilities and big data analytics.

Big Data Analytics and Internet of Things in Industrial Internet in Former Soviet Union Countries

2019 ◽  
pp. 359-378
Author(s):  
Vardan Mkrttchian ◽  
Leyla Ayvarovna Gamidullaeva ◽  
Svetlana Panasenko ◽  
Arman Sargsyan
Keyword(s):  
Big Data ◽  
Soviet Union ◽  
Internet Of Things ◽  
Former Soviet Union ◽  
Big Data Analytics ◽  
Accounting Data ◽  
New Concepts ◽  
Industrial Internet ◽  
Potential Applications ◽  
Data Location

The purpose of this chapter is to explore the integration of three new concepts—big data, internet of things, and internet signs—in the countries of the former Soviet Union. Further, the concept of big data is analyzed. The internet of things is analyzed. Information on semiotics is given, and it reduces to the notion of internet signs. Context concepts and the contribution of big data, internet of things, and internet of signs to contextual simplification are analyzed. The chapter briefly outlines some potential applications of the integration of these three concepts. The chapter briefly discusses the contribution of the study and gives some extensions. These applications included continuous monitoring of accounting data, continuous verification and validation, and use of big data, location information, and other data, for example, to control fraudsters in the countries of the former Soviet Union.

scholarly journals Review of Clinical Research Informatics

2020 ◽  
Vol 29 (01) ◽  
pp. 193-202
Author(s):  
Anthony Solomonides
Keyword(s):  
Artificial Intelligence ◽  
Clinical Research ◽  
Big Data Analytics ◽  
Medical Knowledge ◽  
Data Models ◽  
Research Areas ◽  
Personal Awareness ◽  
Real World Evidence ◽  
Clinical Research Informatics ◽  
Active Research

Objectives: Clinical Research Informatics (CRI) declares its scope in its name, but its content, both in terms of the clinical research it supports—and sometimes initiates—and the methods it has developed over time, reach much further than the name suggests. The goal of this review is to celebrate the extraordinary diversity of activity and of results, not as a prize-giving pageant, but in recognition of the field, the community that both serves and is sustained by it, and of its interdisciplinarity and its international dimension. Methods: Beyond personal awareness of a range of work commensurate with the author’s own research, it is clear that, even with a thorough literature search, a comprehensive review is impossible. Moreover, the field has grown and subdivided to an extent that makes it very hard for one individual to be familiar with every branch or with more than a few branches in any depth. A literature survey was conducted that focused on informatics-related terms in the general biomedical and healthcare literature, and specific concerns (“artificial intelligence”, “data models”, “analytics”, etc.) in the biomedical informatics (BMI) literature. In addition to a selection from the results from these searches, suggestive references within them were also considered. Results: The substantive sections of the paper—Artificial Intelligence, Machine Learning, and “Big Data” Analytics; Common Data Models, Data Quality, and Standards; Phenotyping and Cohort Discovery; Privacy: Deidentification, Distributed Computation, Blockchain; Causal Inference and Real-World Evidence—provide broad coverage of these active research areas, with, no doubt, a bias towards this reviewer’s interests and preferences, landing on a number of papers that stood out in one way or another, or, alternatively, exemplified a particular line of work. Conclusions: CRI is thriving, not only in the familiar major centers of research, but more widely, throughout the world. This is not to pretend that the distribution is uniform, but to highlight the potential for this domain to play a prominent role in supporting progress in medicine, healthcare, and wellbeing everywhere. We conclude with the observation that CRI and its practitioners would make apt stewards of the new medical knowledge that their methods will bring forward.

scholarly journals Big Data on Machine Learning – A Review

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Balasree K ◽  
Dharmarajan K
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Rapid Development ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Big Data Technology

In rapid development of Big Data technology over the recent years, this paper discussing about the Machine Learning (ML) playing role that is based on methods and algorithms to Big Data Processing and Big Data Analytics. In evolutionary fields and computing fields of developments that both are complementing each other. Big Data: The rapid growth of such data solutions needed to be studied and provided to handle then to gain the knowledge from datasets and extracting values due to the data sets are very high in velocity and variety. The Big data analytics are involving and indicating the appropriate data storage and computational outline that enhanced by using Scalable Machine Learning Algorithms and Big Data Analytics then the analytics to reveal the massive amounts of hidden data’s and secret correlations. This type of Analytic information useful for organizations and companies to gain deeper knowledge, development and getting advantages over the competition. When using this Analytics we can predict the accurate implementation over the data. This paper presented about the detailed review of state-of-the-art developments and overview of advantages and challenges in Machine Learning Algorithms over big data analytics.

The 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science (IWCTS 2020)

2021 ◽  
Vol 12 (3) ◽  
pp. 26-31
Author(s):  
Kuldeep Kurte ◽  
Anne Berres ◽  
Srinath Ravulaparthy ◽  
Jibonananda Sanyal ◽  
Gautam Thakur
Keyword(s):  
Big Data ◽  
High Performance ◽  
International Workshop ◽  
Big Data Analytics ◽  
Transportation Systems ◽  
Growth Strategies ◽  
Population Movements ◽  
Flow Models ◽  
Traffic Flow Models ◽  
Near Term

Today's growth in big data, edge computing, high-performance computing, and machine learning has opened tremendous opportunities for advances in mobility. The 13 th International Workshop on Computational Transportation Science (IWCTS 2020) is particularly timely given the prominence of connected automated vehicles technologies in the global auto industry's near-term growth strategies, of big data analytics, unprecedented access to sensing data of mobility, and of integration of this analytics into the optimization of mobility and transport. These developments (as listed below) are deeply computational. • Transportation Planning & Modeling: travel behavioral analysis, modeling and simulation of population movements and freight transportation systems. • Transportation Operations: Connected and Autonomous (CAVs), Computational traffic flow models and control algorithms, role of transportation in community spread of a pandemic (COVID-19). • Infrastructure Sensing: Digital Twin, Data-driven approaches to transportation systems operations • Other Technologies: Urban sensing technologies, Geoinformatics and Regional Science

Big Data, Privacy, and Healthcare

2019 ◽  
pp. 162-175
Author(s):  
Nirav Bhatt ◽  
Amit Thakkar
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
Rapid Development ◽  
Big Data Analytics ◽  
Disease Diagnosis ◽  
Security And Privacy ◽  
Healthcare Industry ◽  
Storage And Retrieval ◽  
Healthcare Data

In the era of big data, large amounts of data are generated from different areas like education, business, stock market, healthcare, etc. Most of the available data from these areas are unstructured, which is large and complex. As healthcare industries become value-based from volume-based, there is a need to have specialized tools and methods to handle it. The traditional methods for data storage and retrieval can be used when data is structured in nature. Big data analytics provide technologies to store large amounts of complex healthcare data. It is believed that there is an enormous opportunity to improve lives by applying big data in the healthcare industry. No industry counts more than healthcare as it is a matter of life and death. Due to rapid development of big data tools and technologies, it is possible to improve disease diagnosis more efficiently than ever before, but security and privacy are two major issues when dealing with big data in the healthcare industry.

Advanced Data Analytics for Clinical Research Part I: What are the Tools?

2020 ◽  
Vol 15 (2) ◽  
pp. 114-119 ◽  
Author(s):  
Nicolas Zhou ◽  
Erin M. Corsini ◽  
Shida Jin ◽  
Gregory R. Barbosa ◽  
Trey Kell ◽  
...  
Keyword(s):  
Big Data ◽  
Clinical Research ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Data Analytics ◽  
Relational Databases ◽  
Big Data Analytics ◽  
Rapid Expansion ◽  
Data Infrastructure

The concept of Big Data is changing the way that clinical research can be performed. Cardiothoracic surgeons need to understand the dynamic digital transformation taking place in the healthcare industry. In the last decade, technological advances and Big Data analytics have become powerful tools for businesses. In healthcare, rapid expansion of Big Data infrastructure has occurred in parallel with attempts to reduce cost and improve outcomes. Many hospitals around the country are augmenting traditional relational databases with Big Data infrastructure. Advanced data capture and categorization tools such as natural language processing and optical character recognition are being developed for clinical and research use, while Internet of Things in the form of wearable technology serves as an additional source of data usable for research. As cardiothoracic surgeons seek ways to innovate, novel approaches to data acquisition and analysis enable a more rigorous level of investigatory efforts.

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