Big Data Analytics for Childhood Pneumonia Monitoring

2016 ◽  
pp. 77-98
Author(s):  
Suresh Kumar Peddoju ◽  
Kavitha K. ◽  
Sharma S. C.
Keyword(s):  
Big Data ◽  
Early Identification ◽  
Big Data Analytics ◽  
Previous Treatment ◽  
Medical Attention ◽  
Appropriate Treatment ◽  
Pediatric Pneumonia ◽  
Increased Risk ◽  
Cause Of Deaths ◽  
History Of

In developing countries pediatric pneumonia is the second leading cause of deaths and 98% of pneumonia-induced deaths are identified across the world. It is mandatory to identify the symptoms of pneumonia in children to avoid mortality causing complications. Early identification of children at risk for treatment failure or at increased risk for death will help to improve overall health outcomes. If pneumonia is suspected, it is important to seek medical attention promptly so that an accurate diagnosis can be made and appropriate treatment is given in time. The proposed approach quickly provides history of previous patient's details, expert doctor's opinions who are in globe and their previous treatment for the same symptoms, all diagnostic reports such as blood tests, x-ray etc., from the cloud and gives analytics from big data to take fast and precise decisions by the doctors.

Big Data Analytics for Childhood Pneumonia Monitoring

Web Services ◽  
2019 ◽  
pp. 1129-1145 ◽  
Author(s):  
Suresh Kumar Peddoju ◽  
Kavitha K. ◽  
Sharma S. C.
Keyword(s):  
Big Data ◽  
Early Identification ◽  
Big Data Analytics ◽  
Previous Treatment ◽  
Medical Attention ◽  
Appropriate Treatment ◽  
Pediatric Pneumonia ◽  
Increased Risk ◽  
Cause Of Deaths ◽  
History Of

In developing countries pediatric pneumonia is the second leading cause of deaths and 98% of pneumonia-induced deaths are identified across the world. It is mandatory to identify the symptoms of pneumonia in children to avoid mortality causing complications. Early identification of children at risk for treatment failure or at increased risk for death will help to improve overall health outcomes. If pneumonia is suspected, it is important to seek medical attention promptly so that an accurate diagnosis can be made and appropriate treatment is given in time. The proposed approach quickly provides history of previous patient's details, expert doctor's opinions who are in globe and their previous treatment for the same symptoms, all diagnostic reports such as blood tests, x-ray etc., from the cloud and gives analytics from big data to take fast and precise decisions by the doctors.

Introduction to Big Data and Business Analytics

2022 ◽  
pp. 67-76
Author(s):  
Dineshkumar Bhagwandas Vaghela
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Sets ◽  
Complex Data ◽  
Business Analytics ◽  
Database Applications ◽  
Complex Data Sets ◽  
History Of ◽  
Rapid Generation

The term big data has come due to rapid generation of data in various organizations. In big data, the big is the buzzword. Here the data are so large and complex that the traditional database applications are not able to process (i.e., they are inadequate to deal with such volume of data). Usually the big data are described by 5Vs (volume, velocity, variety, variability, veracity). The big data can be structured, semi-structured, or unstructured. Big data analytics is the process to uncover hidden patterns, unknown correlations, predict the future values from large and complex data sets. In this chapter, the following topics will be covered more in detail. History of big data and business analytics, big data analytics technologies and tools, and big data analytics uses and challenges.

PGR - 2 Neuropsychology’s Role in Identifying Functional Neurological Disorder

2019 ◽  
Vol 34 (6) ◽  
pp. 828-828
Author(s):  
J Mietchen ◽  
A Kessler-Jones ◽  
P Mission
Keyword(s):  
Neurological Disorder ◽  
Neuropsychological Evaluation ◽  
Performance Validity ◽  
Appropriate Treatment ◽  
Increased Risk ◽  
History Of ◽  
Neuropsychological Evaluations ◽  
Performance Validity Measures ◽  
One Year ◽  
Functional Neurological Disorder

Abstract Objective To outline the usefulness of neuropsychological evaluation in identifying functional neurological disorder. Functional neurological disorder accounts for an estimated 16% of neurology referrals and is a “crisis of neurology” (Edwards & Bhatia, 2012). Adolescents with a history of neurologic compromise, including autoimmune disorders, are at increased risk for comorbid functional neurological disorder (Reuber, Mitchell, Howlett, Crimlisk, & Grünewald, 2005). Method 16-year-old female with a history of Hoshimoto’s encephalopathy referred by her neurologist. Following diagnosis and treatment, she developed a constellation of symptoms, including wide set gait, nystagmus, incontinence, and dystonic episodes. She also reported lapses in memory that lasted a few minutes at a time. During these episodes, she forgot who her mother was and forgot details about her home, which resulted in panic. Results Two neuropsychological evaluations were completed over two years. Psychometric intelligence declined by two standard deviations compared to previous testing one year before. Her performance on memory tasks declined dramatically as well. Despite these declines, there was no decline in activities of daily living. She failed embedded and stand-alone performance validity measures (RDS = 4; TOMM = 29, 30, 28). These findings were described to her neurologist and psychiatrist and we discussed the importance of identifying functional symptoms in the context of her medical history. Conclusions Our evaluation identified significant discrepancies between neuropsychological performance and daily cognitive functioning. The memory lapses she described were inconsistent with any known etiology or illness. Neuropsychological evaluation identified symptoms of a functional nature and assisted in appropriate treatment planning.

scholarly journals Relevance of Big Data for Business and Management. Exploratory Insights (Part I)

2018 ◽  
Vol 13 (2) ◽  
pp. 153-163
Author(s):  
Claudia Ogrean
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Secondary Data ◽  
Supply Side ◽  
Critical Content ◽  
History Of ◽  
The Impact ◽  
Business And Management ◽  
Ubiquitous Presence

AbstractOver the last few decades Big Data has impetuously penetrated almost every domain of human interest/action and it has (more or less consciously) become a ubiquitous presence of day to day life. The main questions this exploratory paper seeks to address (throughout its two parts) are the following: What is the (actual) impact of Big Data on Business & Management and How can businesses (through their management) leverage the potential of Big Data to their benefit? A gradual, step by step approach (based on literature review and a variety of secondary data) will guide the paper in search for answers to the abovementioned questions: starting with a concise history of the topic Big Data as reflected in academia and a critical content analysis of the Big Data concept, the paper will then continue by emphasizing some of the most significant realities and trends that characterize the supply-side of the big data industry; the second part of the paper is dedicated to the investigation of the demand-side of the big data industry – by highlighting some evidences (and projections) on the impact of big data analytics on Business & Management (both at aggregate and granular level) and exploring what companies could and should do (through their management) in order to best capitalize on the opportunities of big data and avoid/minimize the impact of its threats.

Nursing Considerations for Patients With HIV in Critical Care Settings

2020 ◽  
Vol 31 (3) ◽  
pp. 308-317
Author(s):  
Lucy Graham ◽  
Mary Beth Flynn Makic
Keyword(s):  
Critical Care ◽  
Antiretroviral Therapy ◽  
People Living With Hiv ◽  
Secondary Complications ◽  
Qt Intervals ◽  
Appropriate Treatment ◽  
Increased Risk ◽  
History Of ◽  
Living With Hiv

Infection with HIV is a chronic condition that requires daily medication to suppress viral replication. With appropriate treatment, people living with HIV have a life expectancy approaching that of the general population. However, they are at increased risk for comorbidities including cardiovascular disease, renal disease, type 2 diabetes, neurologic conditions, and cancers, often with worse outcomes than in patients without HIV. When they are admitted to critical care settings, care considerations, particularly regarding antiretroviral therapy, must be addressed. Antiretroviral therapy is critical for successful management of HIV infection and should be continued when possible during intensive care unit stays. However, many antiretroviral regimens result in drug-drug interactions, adverse drug-related events, and secondary complications such as insulin resistance and prolonged QT intervals. Critical care nurses have unique opportunities to provide safe, unbiased, and compassionate care that promotes health for a population of people who have a history of being stigmatized.

Systemizing Interprocedural Static Analysis of Large-scale Systems Code with Graspan

2021 ◽  
Vol 38 (1-2) ◽  
pp. 1-39
Author(s):  
Zhiqiang Zuo ◽  
Kai Wang ◽  
Aftab Hussain ◽  
Ardalan Amiri Sani ◽  
Yiyu Zhang ◽  
...  
Keyword(s):  
Big Data ◽  
Static Analysis ◽  
Large Scale ◽  
Big Data Analytics ◽  
Large Scale Systems ◽  
Context Sensitive ◽  
Large Program ◽  
History Of ◽  
Graph System ◽  
Processing Techniques

There is more than a decade-long history of using static analysis to find bugs in systems such as Linux. Most of the existing static analyses developed for these systems are simple checkers that find bugs based on pattern matching. Despite the presence of many sophisticated interprocedural analyses, few of them have been employed to improve checkers for systems code due to their complex implementations and poor scalability. In this article, we revisit the scalability problem of interprocedural static analysis from a “Big Data” perspective. That is, we turn sophisticated code analysis into Big Data analytics and leverage novel data processing techniques to solve this traditional programming language problem. We propose Graspan , a disk-based parallel graph system that uses an edge-pair centric computation model to compute dynamic transitive closures on very large program graphs. We develop two backends for Graspan, namely, Graspan-C running on CPUs and Graspan-G on GPUs, and present their designs in the article. Graspan-C can analyze large-scale systems code on any commodity PC, while, if GPUs are available, Graspan-G can be readily used to achieve orders of magnitude speedup by harnessing a GPU’s massive parallelism. We have implemented fully context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases written in multiple languages such as Linux and Apache Hadoop demonstrates that their Graspan implementations are language-independent, scale to millions of lines of code, and are much simpler than their original implementations. Moreover, we show that these analyses can be used to uncover many real-world bugs in large-scale systems code.

scholarly journals Data Driven Manufacturing: Challenges and Opportunities

2021 ◽  
Author(s):  
Hendro Wicaksono
Keyword(s):  
Big Data ◽  
Manufacturing Sector ◽  
Big Data Analytics ◽  
Data Driven ◽  
Manufacturing Operations ◽  
Smart Products ◽  
Tracking Condition ◽  
Challenges And Opportunities ◽  
History Of ◽  
Design Production

The presentation starts with the history of industrial revolutions, the differences between industry 3.0 and 4.0, the enabling innovation and technologies of industry 4.0. It then explains that smart products, processes, and resources (PPR) that lead to the generation and use of big data. Big data has high implications on both customer-centric activities and operations in the manufacturing sector. Manufacturing operations can be optimized using big data analytics, such as the product or service design, production planning and optimization, material tracking, condition monitoring, quality control, predictive maintenance, and sustainability optimization. Data can revolutionize the industry through data-driven services as add-ons to conventional products. Finally, the presentation presents research and development projects related to data-driven manufacturing, especially for sustainable and collaborative manufacturing.

Serious Suicide Attempts and Risk of Suicide Death

Crisis ◽  
2020 ◽  
pp. 1-8
Author(s):  
Deepak Prabhakar ◽  
Edward L. Peterson ◽  
Yong Hu ◽  
Simran Chawa ◽  
Rebecca C. Rossom ◽  
...  
Keyword(s):  
Suicide Attempt ◽  
Suicide Attempts ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Medical Attention ◽  
Medical Visit ◽  
Suicide Death ◽  
Increased Risk ◽  
The Us ◽  
History Of

Abstract. Background: In the US, more than one million people attempt suicide each year. History of suicide attempt is a significant risk factor for death by suicide; however, there is a paucity of data from the US general population on this relationship. Aim: The objective of this study was to examine suicide attempts needing medical attention as a risk for suicide death. Method: We conducted a case–control study involving eight US healthcare systems. A total of 2,674 individuals who died by suicide from 2000 to 2013 were matched to 267,400 individuals by year and location. Results: Prior suicide attempt associated with a medical visit increases risk for suicide death by 39.1 times, particularly for women ( OR = 79.2). However, only 11.3% of suicide deaths were associated with an attempt that required medical attention. The association was the strongest for children 10–14 years old ( OR = 98.0). Most suicide attempts were recorded during the 20-week period prior to death. Limitations: Our study is limited to suicide attempts for which individuals sought medical care. Conclusion: In the US, prior suicide attempt is associated with an increased risk of suicide death; the risk is high especially during the period immediately following a nonlethal attempt.

scholarly journals Mo1856 – Big Data Analytics Identifies Ulcerative Colitis Patients At Increased Risk for Incident Colorectal Neoplasia Using Multiyear Patterns of Routine Clinical Lab Values

2019 ◽  
Vol 156 (6) ◽  
pp. S-864
Author(s):  
Carlita Shen ◽  
Claudia Ramos Rivers ◽  
Dmitriy Babichenko ◽  
Douglas J. Hartman ◽  
Ioannis Koutroubakis ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Big Data ◽  
Data Analytics ◽  
Colorectal Neoplasia ◽  
Big Data Analytics ◽  
Increased Risk

scholarly journals Data Streaming of Healthcare from Internet of Things (IoTs) using Big Data Analytics

2019 ◽  
Vol IV (I) ◽  
pp. 287-295
Author(s):  
Dost Muhammad Khan ◽  
Muhammad Jameel Sumra ◽  
Faisal Shahzad
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Sector ◽  
Data Streaming ◽  
Continuous Stream ◽  
History Of ◽  
Analytical Processing ◽  
Effectiveness And Efficiency ◽  
Iot Devices

The present study aims at the concept of the IoTs (IoT) and its relation with the healthcare sector. Nowadays, IoT is the main focus of researchers and scientists while this concept illustrates the data stream generated from IoT devices in massive amounts like big data with a continuous stream that requires its proper handling. This study aims at the analytical processing of big datasets having a medical history of patients and their diseases. The data cleansing is applied before going through the analytics phase due to the existence of some noisy and missing data. The analytics of data identified that what events are happening while the mining approaches identified why and how events are happening. Together, both phases help in data analytics and mining. Finally, the analytics and visualization led to the decision making and its results depict the effectiveness and efficiency of the proposed framework for data analytics in IoT

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