scholarly journals A scalable, secure, and interoperable platform for deep data-driven health management

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Amir Bahmani ◽  
Arash Alavi ◽  
Thore Buergel ◽  
Sushil Upadhyayula ◽  
Qiwen Wang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Health Management ◽  
Wearable Sensors ◽  
Data Sets ◽  
Biomedical Data ◽  
Data Types ◽  
Time Data ◽  
Individual Level ◽  
The Individual

AbstractThe large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.

Large-Scale Analysis of Genetic and Clinical Patient Data

2018 ◽  
Vol 1 (1) ◽  
pp. 263-274 ◽  
Author(s):  
Marylyn D. Ritchie
Keyword(s):  
Clinical Data ◽  
Large Scale ◽  
Data Science ◽  
Genomic Analysis ◽  
Genomic Data ◽  
Data Sets ◽  
Biomedical Data ◽  
Data Types ◽  
Phenotypic Data ◽  
Clinical Patient

Biomedical data science has experienced an explosion of new data over the past decade. Abundant genetic and genomic data are increasingly available in large, diverse data sets due to the maturation of modern molecular technologies. Along with these molecular data, dense, rich phenotypic data are also available on comprehensive clinical data sets from health care provider organizations, clinical trials, population health registries, and epidemiologic studies. The methods and approaches for interrogating these large genetic/genomic and clinical data sets continue to evolve rapidly, as our understanding of the questions and challenges continue to emerge. In this review, the state-of-the-art methodologies for genetic/genomic analysis along with complex phenomics will be discussed. This field is changing and adapting to the novel data types made available, as well as technological advances in computation and machine learning. Thus, I will also discuss the future challenges in this exciting and innovative space. The promises of precision medicine rely heavily on the ability to marry complex genetic/genomic data with clinical phenotypes in meaningful ways.

scholarly journals ReactomeFIViz: the Reactome FI Cytoscape app for pathway and network-based data analysis

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 146 ◽  
Author(s):  
Guanming Wu ◽  
Eric Dawson ◽  
Adrian Duong ◽  
Robin Haw ◽  
Lincoln Stein
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Graphical Models ◽  
High Throughput ◽  
Interaction Network ◽  
Large Data ◽  
Relevant Information ◽  
Data Sets ◽  
Data Types ◽  
Biological Studies

High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network and human curated pathways from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.

A Real-Time Log Analyzer Based on MongoDB

2014 ◽  
Vol 571-572 ◽  
pp. 497-501 ◽  
Author(s):  
Qi Lv ◽  
Wei Xie
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Performance Comparison ◽  
Log Analysis ◽  
Data Sets ◽  
Time Data ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Implementation Approach ◽  
And Performance

Real-time log analysis on large scale data is important for applications. Specifically, real-time refers to UI latency within 100ms. Therefore, techniques which efficiently support real-time analysis over large log data sets are desired. MongoDB provides well query performance, aggregation frameworks, and distributed architecture which is suitable for real-time data query and massive log analysis. In this paper, a novel implementation approach for an event driven file log analyzer is presented, and performance comparison of query, scan and aggregation operations over MongoDB, HBase and MySQL is analyzed. Our experimental results show that HBase performs best balanced in all operations, while MongoDB provides less than 10ms query speed in some operations which is most suitable for real-time applications.

Perceived Trust and Confidence for Cryptocurrency Adoption

2022 ◽  
pp. 250-279
Author(s):  
Ewilly Jie Ying Liew ◽  
Wei Li Peh ◽  
Zhuan Kee Leong
Keyword(s):  
Large Scale ◽  
Social Impact ◽  
Public Perceptions ◽  
Middle Income ◽  
Individual Level ◽  
Perceived Trust ◽  
Middle Income Countries ◽  
Regulatory Bodies ◽  
The Individual ◽  
National Databases

This chapter seeks to examine the influence of public perceptions of trust in people and confidence in institutions on cryptocurrency adoption, taking into account the individual-level demographic factors and the regional-level contextual factors. Data is obtained from three large-scale international surveys and national databases and analyzed using R software. The multivariate results demonstrate that individuals' public perceptions of trust and confidence significantly contribute to cryptocurrency adoption. Lower perceived trust in people and higher perceived confidence in civil service and international regulatory bodies increase cryptocurrency adoption, while perceived confidence in political and financial institutions discourages cryptocurrency adoption. Additionally, the univariate results find significant comparisons of gender and perceived trust differences on the predictors of cryptocurrency adoption. This chapter discusses and provides insights on the social impact and future of cryptocurrency adoption, particularly among the upper- and lower-middle-income countries.

Pattern Recognition for Large-Scale Data Processing

2015 ◽  
pp. 198-208 ◽  
Author(s):  
Amir Basirat ◽  
Asad I. Khan ◽  
Heinz W. Schmidt
Keyword(s):  
Large Scale ◽  
Distributed Processing ◽  
Data Sets ◽  
Distributed Data ◽  
Time Data ◽  
Deterministic Learning ◽  
Large Scale Data ◽  
Future Data ◽  
Large Scale Data Processing ◽  
Learning Schemes

One of the main challenges for large-scale computer clouds dealing with massive real-time data is in coping with the rate at which unprocessed data is being accumulated. Transforming big data into valuable information requires a fundamental re-think of the way in which future data management models will need to be developed on the Internet. Unlike the existing relational schemes, pattern-matching approaches can analyze data in similar ways to which our brain links information. Such interactions when implemented in voluminous data clouds can assist in finding overarching relations in complex and highly distributed data sets. In this chapter, a different perspective of data recognition is considered. Rather than looking at conventional approaches, such as statistical computations and deterministic learning schemes, this chapter focuses on distributed processing approach for scalable data recognition and processing.

Research Challenges in Big Data Analytics

2017 ◽  
pp. 83-99
Author(s):  
Sivamathi Chokkalingam ◽  
Vijayarani S.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
New Technologies ◽  
Big Data Analytics ◽  
Large Data ◽  
Data Sets ◽  
Data Types ◽  
Customer Preferences ◽  
Research Challenges

The term Big Data refers to large-scale information management and analysis technologies that exceed the capability of traditional data processing technologies. Big Data is differentiated from traditional technologies in three ways: volume, velocity and variety of data. Big data analytics is the process of analyzing large data sets which contains a variety of data types to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. Since Big Data is new emerging field, there is a need for development of new technologies and algorithms for handling big data. The main objective of this paper is to provide knowledge about various research challenges of Big Data analytics. A brief overview of various types of Big Data analytics is discussed in this paper. For each analytics, the paper describes process steps and tools. A banking application is given for each analytics. Some of research challenges and possible solutions for those challenges of big data analytics are also discussed.

Children’s Height and Parental Unemployment: A Large-Scale Anthropometric Study on Eastern Germany, 1994–2006

2010 ◽  
Vol 11 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Jörg Baten ◽  
Andreas Böhm
Keyword(s):  
Employment Status ◽  
Large Scale ◽  
Psychological Effects ◽  
Average Height ◽  
Panel Analysis ◽  
Individual Level ◽  
Parental Unemployment ◽  
The Individual ◽  
Anthropometric Study

Abstract The average height of children is an indicator of the quality of nutrition and healthcare. In this study, we assess the effect of unemployment and other factors on this variable. In the Eastern German Land of Brandenburg, a dataset of 253,050 preschool height measurements was compiled and complemented with information on parents’ schooling and employment status. Unemployment might have negative psychological effects, with an impact on parental care. Both a panel analysis of districts and an assessment at the individual level yield the result that increasing unemployment, net out-migration and fertility were in fact reducing height.

INFERENCE OF BIOMEDICAL DATA SETS USING BAYESIAN MACHINE LEARNING

2019 ◽  
Vol 31 (04) ◽  
pp. 1950030
Author(s):  
Ayesha Sohail
Keyword(s):  
Bayesian Analysis ◽  
Bayesian Methods ◽  
Relevant Information ◽  
Data Sets ◽  
Hybrid Technique ◽  
Biomedical Data ◽  
Bayesian Techniques ◽  
Clinical Databases ◽  
The Individual ◽  
Available Information

Due to the advancement in data collection and maintenance strategies, the current clinical databases around the globe are rich in a sense that these contain detailed information not only about the individual’s medical conditions, but also about the environmental features, associated with the individual. Classification within this data could provide new medical insights. Data mining technology has become an attraction for researchers due to its affectivity and efficacy in the field of biomedicine research. Due to the diverse structure of such data sets, only few successful techniques and easy to use softwares, are available in literature. A Bayesian analysis provides a more intuitive statement of probability that hypothesis is true. Bayesian approach uses all available information and can give answers to complex questions more accurately. This means that Bayesian methods include prior information. In Bayesian analysis, no relevant information is excluded as prior represents all the available information apart from data itself. Bayesian techniques are specifically used for decision making. Uncertainty is the main hurdle in making decisions. Due to lack of information about relevant parameters, there is uncertainty about given decision. Bayesian methods measure these uncertainties by using probability. In this study, selected techniques of biostatistical Bayesian inference (the probability based inferencing approach, to identify uncertainty in databases) are discussed. To show the efficiency of a Hybrid technique, its application on two distinct data sets is presented in a novel way.

N = 1 Research Methodology in Counselling: Focusing upon the Individual Client

2003 ◽  
Vol 13 (2) ◽  
pp. 133-144 ◽  
Author(s):  
Christopher F. Sharpley
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Single Subject ◽  
Health Practitioner ◽  
Design Data ◽  
Single Subject Research ◽  
Research Designs ◽  
Research Findings ◽  
Individual Client ◽  
The Individual

Although the last 20years have seen a focus upon evidence-based therapies, there are arguments that much of the so-called “evidence” produced is, in fact, irrelevant to the mental health practitioner in the field, principally because of the use of large-scale group designs in clinical controlled studies of the effectiveness of one therapy over another. By contrast, and with particular relevance to the practitioner who is both scientist and therapist, single subject research designs and methodologies for data analysis can be applied in ways that allow for generalisation to everyday practice. To inform the readership, the rationale underlying n = 1 studies is described, with some explanation of the major designs and their application to typical cases in guidance and counselling. Issues of inferential deductions from data, variations of design, data analysis via visual and statistical procedures, and replication are discussed. Finally, a case is argued for the introduction of n = 1 reports within the Australian Journal of Guidance and Counselling to better inform the readership about clinical research findings relevant to their practices.

The Effect of Public Service Motivation at Individual, Group, and Organisational Levels of Citizenship Behaviour

2020 ◽  
Vol 33 (1) ◽  
pp. 39-58
Author(s):  
Kuo-Tai Cheng ◽  
Yuan-Chieh Chang ◽  
Changyen Lee
Keyword(s):  
Public Service ◽  
Organizational Citizenship Behavior ◽  
Large Scale ◽  
Public Service Motivation ◽  
Employee Attitudes ◽  
Public Utility ◽  
Citizenship Behavior ◽  
Perceived Effectiveness ◽  
Individual Level ◽  
The Individual

This study conceptualizes and empirically investigates how dimensions of public service motivation affect perceived citizenship behaviour in the context of government-owned utilities. This study used a large-scale questionnaire survey from four utility sectors in Taiwan (N = 1,087). The emergent model suggests that compassion (COM) and self-sacrifice (SS) affect the perceived effectiveness of individual-level Organizational Citizenship Behavior (OCB). Commitment to the Public Interest (CPI) and Attraction to Public Policy making (APP) affect perceived effectiveness of OCB at the group and organisational levels, respectively. The results support the expected contribution of OCB, from the individual to the group levels, and from the group level to the organisational level. Public utility managers should strive to improve employee attitudes and motivation towards greater levels of OCB.

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