Predicting Asthma-Related Emergency Department Visits Using Big Data

Ram, Sudha; Zhang, Wenli; Williams, Max W.; Pengetnze, Yolande

doi:10.1109/jbhi.2015.2404829

Cited by 158 publications

(80 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Early detection of disease outbreaks is not a new issue but it remains crucial. In 2015, still many researchers [8] [9][10] tried to develop the ultimate surveillance system using search engines, social network, or wiki data. While authors combined more and more data sources for their surveillance systems, the use of health system data remained limited.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Some Innovative Approaches for Public Health and Epidemiology Informatics

Toubiana

Griffon

2016

Yearb Med Inform

View full text Add to dashboard Cite

SummaryObjectives: Summarize excellent current research published in 2015 in the field of Public Health and Epidemiology Informatics. Methods: The complete 2015 literature concerning public health and epidemiology informatics has been searched in PubMed and Web of Science, and the returned references were reviewed by the two section editors to select 14 candidate best papers. These papers were then peer-reviewed by external reviewers to allow the editorial team an enlightened selection of the best papers. Results: Among the 1,272 references retrieved from PubMed and Web of Science, three were finally selected as best papers. The first one presents a language agnostic approach for epidemic event detection in news articles. The second paper describes a system using big health data gathered by a statewide system to forecast emergency department visits. The last paper proposes a rather original approach that uses machine learning to solve the old issue of outbreak detection and prediction. Conclusions: The increasing availability of data, now directly from health systems, will probably lead to a boom in public health surveillance systems and in large-scale epidemiologic studies.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Ram et al [8] aggregated data from Google and Twitter with air pollution data to predict asthma-related ED visits. Unlike Hu et al [5], the researchers had to be satisfied with data from one hospital.…”

Section: Discussionmentioning

confidence: 99%

Some Innovative Approaches for Public Health and Epidemiology Informatics

Toubiana

Griffon

2016

Yearb Med Inform

View full text Add to dashboard Cite

show abstract

“…Prior literature has consistently suggested that approximately 1% of tweets are geotagged, thus providing the data on latitude and longitude of the locations where the tweets are posted (e.g., Jahanbakhsh & Moon, 2014;Mislove, Lehmann, Ahn, Onnela, & Rosenquist, 2012;Ram et al, 2015;Young, Rivers, & Lewis, 2014). In addition, around 70% of Twitter users self-report their geographic locations on their Twitter profiles (e.g., Mislove et al, 2012;.…”

Section: Sentiment Analysismentioning

confidence: 99%

Education policy research in the big data era: Methodological frontiers, misconceptions, and challenges

Wang

2017

EPAA

View full text Add to dashboard Cite

Abstract:Despite abundant data and increasing data availability brought by technological advances, there has been very limited education policy studies that have capitalized on big data-characterized by large volume, wide variety, and high velocity. Drawing on the recent progress of using big data in public policy and computational social science research, this commentary discusses how to approach big data and how big data can be used in education policy research. First, I introduce big data that is potentially relevant to education policy research. I then present methodological frontiers by examining the assumptions, key concepts, merits, and caveats of three commonly used analytical approaches to mining massive amounts of text data: topic models, network text analysis, and sentiment analysis. Next, to ensure the veracity of using big data in education policy research, I debunk three methodological misconceptions. This commentary concludes with a discussion on developing interdisciplinary research capacity and addressing the privacy concerns and ethical conundrums as we explore a research agenda of using big data in education policy. Keywords: big data; education policy; network text analysis; sentiment analysis; text mining; topic models Investigación de política educativa en la era de los grandes datos: Fronteras metodológicas, equívocos, y desafíos Resumen: A pesar de la abundancia de datos y del aumento de la disponibilidad de datos traídos por los avances tecnológicos, hubo estudios de políticas educativas muy limitadas que usaron datos importantes, caracterizados por gran volumen, gran variedad y alta velocidad. En base al reciente progreso del uso de grandes datos en investigaciones de políticas públicas y de ciencia social computacional, este trabajo pretende demostrar el potencial de datos importantes y la gran cantidad de datos que pueden ser utilizados en la investigación de políticas educativas. En primer lugar, introduzca datos importantes que son potencialmente relevantes para la investigación de políticas educativas. Puedo, entonces, presentar fronteras metodológicas, examinando los supuestos conceptos clave, méritos y salvedades de tres enfoques analíticos comúnmente usados en la minería de grandes cantidades de datos de texto: modelos de tópicos, análisis de conexiones textuales y análisis de sentimientos. A continuación, para garantizar la veracidad del uso de grandes datos en la investigación sobre políticas educativas, desenmascaramos tres equívocos metodológicos. Este artículo concluye con una discusión sobre el desarrollo de la capacidad de investigación interdisciplinaria abordando las preocupaciones de privacidad y los enigmas éticos a medida que exploramos una agenda de investigación de uso de datos importantes en la política educativa. Palabras-clave: datos grandes; política educativa; análisis de conexiones textuales; análisis de sentimientos; minería de textos; modelos de temas Pesquisa de política educacional na era dos grandes dados: Fronteiras metodológicas, equívocos, e desafios ...

show abstract

“…In addition, social media data have been used to improve our understanding of Ebola [12] and Zika virus infections [13][14][15]. While disease surveillance efforts tend to focus on acute infectious diseases, studies have also been conducted on chronic diseases such as cancer [16], hypertension [16], asthma [16][17][18], diabetes [19], and seasonal allergic rhinitis (SAR) [20][21][22]. Systematic reviews have also been conducted on disease surveillance based on social media data [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis (Preprint)

Wakamiya¹,

Matsune²,

Okubo³

et al. 2018

Preprint

View full text Add to dashboard Cite

Background: Health-related social media data are increasingly used in disease surveillance studies, which have demonstrated moderately high correlations between the number of social media posts and the number of patients. However, there is a need to understand the causal relationship between the behavior of social media users and the actual number of patients to increase the credibility of disease surveillance based on social media data. Objective: To clarify the causal relationships among pollen count, the posting behavior of social media users, and the number of SAR patients in the real world. Methods: This analysis was conducted using datasets of pollen count, tweet numbers, and SAR patient numbers from Kanagawa Prefecture, Japan. We examined daily pollen counts for Japanese cedar (the major cause of SAR in Japan) and hinoki cypress (which commonly complicates SAR) from February 1 to May 31, 2017. The daily numbers of tweets that included the keyword "kafunshō" (or SAR) were calculated between January 1 and May 31, 2017. Daily SAR patient numbers from January 1 to May 31, 2017 were obtained from three healthcare institutes that participated in the study. The Granger causality test was used to examine the causal relationships among pollen count, tweet numbers, and the number of SAR patients during the study period from February to May 2017. To determine if time-variant factors affect these causal relationships, we also analyzed the main SAR phase (February to April) when Japanese cedar trees actively produce and release pollen. Results: Increases in pollen count were found to cause increases in the number of tweets during the overall study period (P = .04), but not the main SAR phase (P = . 05). In contrast, increases in pollen count were found to increase patient numbers in both the study period (P = .04) and the main SAR phase (P = .01). In addition, increases in the number of tweets also caused increases in patient numbers during the main SAR phase (P = .02), but not the overall study period (P = .89). Patient numbers did not affect the number of tweets in both the overall study period (P = . 24) and the main SAR phase (P = .47). Conclusions: Understanding the causal relationships between these factors is an important step to increase the credibility of surveillance systems that use social media data. Further in-depth studies are needed to identify the determinants of social media posts described in this exploratory analysis.

show abstract

Predicting Asthma-Related Emergency Department Visits Using Big Data

Cited by 158 publications

References 17 publications

Some Innovative Approaches for Public Health and Epidemiology Informatics

Some Innovative Approaches for Public Health and Epidemiology Informatics

Education policy research in the big data era: Methodological frontiers, misconceptions, and challenges

Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis (Preprint)

Contact Info

Product

Resources

About