Big Data Applications in Health Care and Education
01 Jan 2018-pp 203-219
TL;DR: In this article, the authors make a critical analysis of these aspects of higher education and healthcare with respect to big data analysis and make some recommendations in this direction, which can collectively help the healthcare industry to find out problems related to variability in healthcare quality and escalating healthcare expenditure.
Abstract: Technology plays a major role in all spheres of life and higher education and health care are no exceptions. The use of big data in higher education and health care are relatively new. The dynamics of higher education is passing through a phase of rapid changes. Also, the amount of data available in this field and proper analytics can reap the benefits and highlight on future techniques to be followed in handling the complex situations arisen from pressure exerted by accrediting agencies, governments and other stake holders. Higher education is becoming more and more complex with several institutes entering into the market with more and more diversified approaches. This makes the functionalities of all institutes of higher education to revise their approaches frequently to cope up with this pressure. The educational institutes have to ensure that the quality of learning programmes is at par with that of their counterparts at the national and global level. Analysis of vast data sources generated in this connection being more often not available for analysis is a major concern. The analysis of these volumes of data plays a major role in understanding and ensuring that institutions are aware of the changes occurring everywhere and they are taking care of their social responsibilities. Due to digitization of medical records in an attempt to make them available for research and development over the past ten to fifteen years, there is a huge amount of data, which besides being voluminous are complex, diverse and temporal which is collected by healthcare stockholders. An analysis of these data could collectively help the healthcare industry to find out problems related to variability in healthcare quality and escalating healthcare expenditure. In this chapter we shall make a critical analysis of these aspects of higher education and healthcare with respect to big data analysis and make some recommendations in this direction.
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TL;DR: This review presents an overview of the dynamically developing field of mass spectrometry-based metabolomics, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes.
Abstract: This review presents an overview of the dynamically developing field of mass spectrometry-based metabolomics. Metabolomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. These numerous analytes have very diverse physico-chemical properties and occur at different abundance levels. Consequently, comprehensive metabolomics investigations are primarily a challenge for analytical chemistry and specifically mass spectrometry has vast potential as a tool for this type of investigation. Metabolomics require special approaches for sample preparation, separation, and mass spectrometric analysis. Current examples of those approaches are described in this review. It primarily focuses on metabolic fingerprinting, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes. To perform this complex task, data analysis tools, metabolite libraries, and databases are required. Therefore, recent advances in metabolomics bioinformatics are also discussed.
1,954 citations
01 Nov 2010
TL;DR: The most relevant studies carried out in educational data mining to date are surveyed and the different groups of user, types of educational environments, and the data they provide are described.
Abstract: Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.
1,723 citations
TL;DR: The use of information embedded in the Twitter stream is examined to (1) track rapidly-evolving public sentiment with respect to H1N1 or swine flu, and (2) track and measure actual disease activity.
Abstract: Twitter is a free social networking and micro-blogging service that enables its millions of users to send and read each other's “tweets,” or short, 140-character messages. The service has more than 190 million registered users and processes about 55 million tweets per day. Useful information about news and geopolitical events lies embedded in the Twitter stream, which embodies, in the aggregate, Twitter users' perspectives and reactions to current events. By virtue of sheer volume, content embedded in the Twitter stream may be useful for tracking or even forecasting behavior if it can be extracted in an efficient manner. In this study, we examine the use of information embedded in the Twitter stream to (1) track rapidly-evolving public sentiment with respect to H1N1 or swine flu, and (2) track and measure actual disease activity. We also show that Twitter can be used as a measure of public interest or concern about health-related events. Our results show that estimates of influenza-like illness derived from Twitter chatter accurately track reported disease levels.
1,195 citations
11 Aug 2013
TL;DR: This paper infer the real-time and fine-grained air quality information throughout a city, based on the (historical and real- time) air quality data reported by existing monitor stations and a variety of data sources the authors observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs).
Abstract: Information about urban air quality, e.g., the concentration of PM2.5, is of great importance to protect human health and control air pollution. While there are limited air-quality-monitor-stations in a city, air quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, traffic volume, and land uses. In this paper, we infer the real-time and fine-grained air quality information throughout a city, based on the (historical and real-time) air quality data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs). We propose a semi-supervised learning approach based on a co-training framework that consists of two separated classifiers. One is a spatial classifier based on an artificial neural network (ANN), which takes spatially-related features (e.g., the density of POIs and length of highways) as input to model the spatial correlation between air qualities of different locations. The other is a temporal classifier based on a linear-chain conditional random field (CRF), involving temporally-related features (e.g., traffic and meteorology) to model the temporal dependency of air quality in a location. We evaluated our approach with extensive experiments based on five real data sources obtained in Beijing and Shanghai. The results show the advantages of our method over four categories of baselines, including linear/Gaussian interpolations, classical dispersion models, well-known classification models like decision tree and CRF, and ANN.
829 citations
TL;DR: The proportion of SARI/ARDS cases and deaths due to influenza A(H1N1)pdm09 infection and the impact of other respiratory viruses during pandemic and postpandemic period (2009–2011) in northern Italy is evaluated and a virus discovery technique (VIDISCA-454) enabled the identification of one previously undiagnosed measles infection.
Abstract: Since 2009 pandemic, international health authorities recommended monitoring severe and complicated cases of respiratory disease, that is, severe acute respiratory infection (SARI) and acute respiratory distress syndrome (ARDS). We evaluated the proportion of SARI/ARDS cases and deaths due to influenza A(H1N1)pdm09 infection and the impact of other respiratory viruses during pandemic and postpandemic period (2009–2011) in northern Italy; additionally we searched for unknown viruses in those cases for which diagnosis remained negative. 206 respiratory samples were collected from SARI/ARDS cases and analyzed by real-time RT-PCR/PCR to investigate influenza viruses and other common respiratory pathogens; also, a virus discovery technique (VIDISCA-454) was applied on those samples tested negative to all pathogens. Influenza A(H1N1)pdm09 virus was detected in 58.3% of specimens, with a case fatality rate of 11.3%. The impact of other respiratory viruses was 19.4%, and the most commonly detected viruses were human rhinovirus/enterovirus and influenza A(H3N2). VIDISCA-454 enabled the identification of one previously undiagnosed measles infection. Nearly 22% of SARI/ARDS cases did not obtain a definite diagnosis. In clinical practice, great efforts should be dedicated to improving the diagnosis of severe respiratory disease; the introduction of innovative molecular technologies, as VIDISCA-454, will certainly help in reducing such “diagnostic gap.”
662 citations