Intelligent surveillance lifecycle architecture for epidemiological data clustering using Twitter and novel genetic algorithm

El-Bathy, Naser; Gloster, Clay; Azar, Ghassan; El-Bathy, Mohammed; Stein, Gordon; Stevenson, Ricky

doi:10.1109/eit.2014.6871753

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…The tweet is then classified as disease-related or unrelated based on the clusters' similarity measures. N. El-bathy, C. Gloster, M. El-bathy et al [66] proposed a surveillance lifecycle architecture using a novel genetic algorithm to get relevant data from the large set of online data accessible faster at a lower cost. Health problems such as respiratory, gastrointestinal, heat-related illness, and ILI symptoms circulating among the population during mass gathering were also detected using Twitter [67].…”

Section: Twittermentioning

confidence: 99%

Social media based surveillance systems for healthcare using machine learning: A systematic review

Gupta

Katarya

2020

Journal of Biomedical Informatics

144

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Background: Real-time surveillance in the field of health informatics has emerged as a growing domain of interest among worldwide researchers. Evolution in this field has helped in the introduction of various initiatives related to public health informatics. Surveillance systems in the area of health informatics utilizing social media information have been developed for early prediction of disease outbreaks and to monitor diseases. In the past few years, the availability of social media data, particularly Twitter data, enabled real-time syndromic surveillance that provides immediate analysis and instant feedback to those who are charged with follow-ups and investigation of potential outbreaks. In this paper, we review the recent work, trends, and machine learning(ML) text classification approaches used by surveillance systems seeking social media data in the healthcare domain. We also highlight the limitations and challenges followed by possible future directions that can be taken further in this domain. Methods: To study the landscape of research in health informatics performing surveillance of the various healthrelated data posted on social media or web-based platforms, we present a bibliometric analysis of the 1240 publications indexed in multiple scientific databases (IEEE, ACM Digital Library, ScienceDirect, PubMed) from the year 2010-2018. The papers were further reviewed based on the various machine learning algorithms used for analyzing health-related text posted on social media platforms. Findings:: Based on the corpus of 148 selected articles, the study finds the types of social media or web-based platforms used for surveillance in the healthcare domain, along with the health topic(s) studied by them. In the corpus of selected articles, we found 26 articles were using machine learning technique. These articles were studied to find commonly used ML techniques. The majority of studies (24%) focused on the surveillance of flu or influenza-like illness (ILI). Twitter (64%) is the most popular data source to perform surveillance research using social media text data, and Support Vector Machine (SVM) (33%) being the most used ML algorithm for text classification. Conclusions: The inclusion of online data in surveillance systems has improved the disease prediction ability over traditional syndromic surveillance systems. However, social media based surveillance systems have many limitations and challenges, including noise, demographic bias, privacy issues, etc. Our paper mentions future directions, which can be useful for researchers working in the area. Researchers can use this paper as a library for social media based surveillance systems in the healthcare domain and can expand such systems by incorporating the future works discussed in our paper. period before the confirmation of a particular disease by any clinical unit or laboratory and to mobilize the rapid response. Surveillance systems are usually concerned with the systematic collection, analysis, and interpretation of the collected data along with the...

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Section: Twittermentioning

confidence: 99%

Social media based surveillance systems for healthcare using machine learning: A systematic review

Gupta

Katarya

2020

Journal of Biomedical Informatics

144

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“…While initial studies have shown that tools that make use of "proxy" datasets can serve as useful monitors for emerging diseases [ 14 , 8 , 17 ], recent studies have demonstrated that the estimates from internet search patterns can over-estimate the severity of the outbreak [ 18 , 19 ]. Self-reporting tools such as micro-blogging and social media are also becoming effective proxies for public health surveillance [ 20 - 24 ], although such datasets also have relatively higher noise and teasing out relevant information for specific disease conditions can be quite challenging [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

ORBiT: Oak Ridge biosurveillance toolkit for public health dynamics

et al. 2015

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BackgroundThe digitization of health-related information through electronic health records (EHR) and electronic healthcare reimbursement claims and the continued growth of self-reported health information through social media provides both tremendous opportunities and challenges in developing effective biosurveillance tools. With novel emerging infectious diseases being reported across different parts of the world, there is a need to build systems that can track, monitor and report such events in a timely manner. Further, it is also important to identify susceptible geographic regions and populations where emerging diseases may have a significant impact.MethodsIn this paper, we present an overview of Oak Ridge Biosurveillance Toolkit (ORBiT), which we have developed specifically to address data analytic challenges in the realm of public health surveillance. In particular, ORBiT provides an extensible environment to pull together diverse, large-scale datasets and analyze them to identify spatial and temporal patterns for various biosurveillance-related tasks.ResultsWe demonstrate the utility of ORBiT in automatically extracting a small number of spatial and temporal patterns during the 2009-2010 pandemic H1N1 flu season using claims data. These patterns provide quantitative insights into the dynamics of how the pandemic flu spread across different parts of the country. We discovered that the claims data exhibits multi-scale patterns from which we could identify a small number of states in the United States (US) that act as "bridge regions" contributing to one or more specific influenza spread patterns. Similar to previous studies, the patterns show that the south-eastern regions of the US were widely affected by the H1N1 flu pandemic. Several of these south-eastern states act as bridge regions, which connect the north-east and central US in terms of flu occurrences.ConclusionsThese quantitative insights show how the claims data combined with novel analytical techniques can provide important information to decision makers when an epidemic spreads throughout the country. Taken together ORBiT provides a scalable and extensible platform for public health surveillance.

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Automatic clustering using an improved artificial bee colony optimization for customer segmentation

Kuo

Zulvia

2018

Knowl Inf Syst

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Intelligent surveillance lifecycle architecture for epidemiological data clustering using Twitter and novel genetic algorithm

Cited by 3 publications

References 14 publications

Social media based surveillance systems for healthcare using machine learning: A systematic review

Social media based surveillance systems for healthcare using machine learning: A systematic review

ORBiT: Oak Ridge biosurveillance toolkit for public health dynamics

Automatic clustering using an improved artificial bee colony optimization for customer segmentation

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