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Data
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Big Data and Digital Health
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Big Data and Digital Health

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 77641

Special Issue Editors

Dr. Kelvin Tsoi

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Guest Editor
Stanley Ho Big Data Decision Analytics Research Centre, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
Ms. Karen Kar Lum Yiu

E-Mail
Assistant Guest Editor
Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Hong Kong, China
Mr. Heracles King Hang Lee

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Assistant Guest Editor
Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Hong Kong, China

Special Issue Information

Dear Colleagues,

The digital revolution has transformed the reality of what healthcare means today. Wearable technologies, automated clinical laboratories, sensors, genomics, and various other wireless devices have changed the way healthcare is delivered, setting the path for a digital health future that is increasingly drawing attention from both academia and industry. The key driver of this revolution will be the data. Medical data have become complex and analyses of these data have brought new discoveries and challenges, allowing us to develop new insights and expand our knowledge in the field. It is an exciting time to bring in new discussion topics and ideas on big data and digital health as data become a critical part of our day-to-day lives.

This Special Issue is intended to present discussions and any advances at the intersection of digital health and big data. We would like to invite you to submit articles addressing the digital health ecosystem and the extent to which new technologies can push the health industry forward. Topics include, but are not limited to, the following areas:

  • Artificial intelligence in healthcare
  • Digital health and the aging population
  • Digital analytics for behavioral health
  • Machine learning application for medical research
  • Impact of digital health on processes in the industry

Dr. Kelvin Tsoi
Guest Editor

Ms. Karen Kar Lum Yiu
Mr. Heracles King Hang Lee
Assistant Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

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12 pages, 242 KiB  
Article
Pedagogical Demonstration of Twitter Data Analysis: A Case Study of World AIDS Day, 2014
by Isaac Chun-Hai Fung, Jingjing Yin, Keisha D. Pressley, Carmen H. Duke, Chen Mo, Hai Liang, King-Wa Fu, Zion Tsz Ho Tse and Su-I Hou
Data 2019, 4(2), 84; https://doi.org/10.3390/data4020084 - 10 Jun 2019
Cited by 13 | Viewed by 3627
Abstract
As a pedagogical demonstration of Twitter data analysis, a case study of HIV/AIDS-related tweets around World AIDS Day, 2014, was presented. This study examined if Twitter users from countries with various income levels responded differently to World AIDS Day. The performance of support [...] Read more.
As a pedagogical demonstration of Twitter data analysis, a case study of HIV/AIDS-related tweets around World AIDS Day, 2014, was presented. This study examined if Twitter users from countries with various income levels responded differently to World AIDS Day. The performance of support vector machine (SVM) models as classifiers of relevant tweets was evaluated. A manual coding of 1,826 randomly sampled HIV/AIDS-related original tweets from November 30 through December 2, 2014 was completed. Logistic regression was applied to analyze the association between the World Bank-designated income level of users’ self-reported countries and Twitter contents. To identify the optimal SVM model, 1278 (70%) of the 1826 sampled tweets were randomly selected as the training set, and 548 (30%) served as the test set. Another 180 tweets were separately sampled and coded as the held-out dataset. Compared with tweets from low-income countries, tweets from the Organization for Economic Cooperation and Development countries had 60% lower odds to mention epidemiology (adjusted odds ratio, aOR = 0.404; 95% CI: 0.166, 0.981) and three times the odds to mention compassion/support (aOR = 3.080; 95% CI: 1.179, 8.047). Tweets from lower-middle-income countries had 79% lower odds than tweets from low-income countries to mention HIV-affected sub-populations (aOR = 0.213; 95% CI: 0.068, 0.664). The optimal SVM model was able to identify relevant tweets from the held-out dataset of 180 tweets with an accuracy (F1 score) of 0.72. This study demonstrated how students can be taught to analyze Twitter data using manual coding, regression models, and SVM models. Full article
(This article belongs to the Special Issue Big Data and Digital Health)

Review

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20 pages, 2161 KiB  
Review
Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response
by Sophie E. Jordan, Sierra E. Hovet, Isaac Chun-Hai Fung, Hai Liang, King-Wa Fu and Zion Tsz Ho Tse
Data 2019, 4(1), 6; https://doi.org/10.3390/data4010006 - 29 Dec 2018
Cited by 95 | Viewed by 13221
Abstract
Twitter is a social media platform where over 500 million people worldwide publish their ideas and discuss diverse topics, including their health conditions and public health events. Twitter has proved to be an important source of health-related information on the Internet, given the [...] Read more.
Twitter is a social media platform where over 500 million people worldwide publish their ideas and discuss diverse topics, including their health conditions and public health events. Twitter has proved to be an important source of health-related information on the Internet, given the amount of information that is shared by both citizens and official sources. Twitter provides researchers with a real-time source of public health information on a global scale, and can be very important in public health research. Classifying Twitter data into topics or categories is helpful to better understand how users react and communicate. A literature review is presented on the use of mining Twitter data or similar short-text datasets for public health applications. Each method is analyzed for ways to use Twitter data in public health surveillance. Papers in which Twitter content was classified according to users or tweets for better surveillance of public health were selected for review. Only papers published between 2010–2017 were considered. The reviewed publications are distinguished by the methods that were used to categorize the Twitter content in different ways. While comparing studies is difficult due to the number of different methods that have been used for applying Twitter and interpreting data, this state-of-the-art review demonstrates the vast potential of utilizing Twitter for public health surveillance purposes. Full article
(This article belongs to the Special Issue Big Data and Digital Health)
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Other

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16 pages, 3264 KiB  
Data Descriptor
A Dataset of Students’ Mental Health and Help-Seeking Behaviors in a Multicultural Environment
by Minh-Hoang Nguyen, Manh-Toan Ho, Quynh-Yen T. Nguyen and Quan-Hoang Vuong
Data 2019, 4(3), 124; https://doi.org/10.3390/data4030124 - 21 Aug 2019
Cited by 19 | Viewed by 29954
Abstract
University students, especially international students, possess a higher risk of mental health problems than the general population. However, the literature regarding the prevalence and determinants of mental health problems as well as help-seeking behaviors of international and domestic students in Japan seems to [...] Read more.
University students, especially international students, possess a higher risk of mental health problems than the general population. However, the literature regarding the prevalence and determinants of mental health problems as well as help-seeking behaviors of international and domestic students in Japan seems to be limited. This dataset contains 268 records of depression, acculturative stress, social connectedness, and help-seeking behaviors reported by international and domestic students at an international university in Japan. One of the main findings that can be drawn from this dataset is how the level of social connectedness and acculturative stress are predictive of the reported depression among international as well as domestic students. The dataset is expected to provide reliable materials for further study of cross-cultural public health studies and policy-making in higher education. Full article
(This article belongs to the Special Issue Big Data and Digital Health)
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14 pages, 4645 KiB  
Data Descriptor
Health Care, Medical Insurance, and Economic Destitution: A Dataset of 1042 Stories
by Manh-Toan Ho, Viet-Phuong La, Minh-Hoang Nguyen, Thu-Trang Vuong, Kien-Cuong P. Nghiem, Trung Tran, Hong-Kong T. Nguyen and Quan-Hoang Vuong
Data 2019, 4(2), 57; https://doi.org/10.3390/data4020057 - 27 Apr 2019
Cited by 11 | Viewed by 10699
Abstract
The dataset contains 1042 records obtained from inpatients at hospitals in the northern region of Vietnam. The survey process lasted 20 months from August 2014 to March 2016, and yielded a comprehensive set of records of inpatients’ financial situations, healthcare, and health insurance [...] Read more.
The dataset contains 1042 records obtained from inpatients at hospitals in the northern region of Vietnam. The survey process lasted 20 months from August 2014 to March 2016, and yielded a comprehensive set of records of inpatients’ financial situations, healthcare, and health insurance information, as well as their perspectives on treatment service in the hospitals. Five articles were published based on the smaller subsets. This data article introduces the full dataset for the first time and suggests a new Bayesian statistics approach for data analysis. The full dataset is expected to contribute new data for health economic researchers and new grounded scientific results for policymakers. Full article
(This article belongs to the Special Issue Big Data and Digital Health)
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6 pages, 588 KiB  
Data Descriptor
Electroencephalograms during Mental Arithmetic Task Performance
by Igor Zyma, Sergii Tukaev, Ivan Seleznov, Ken Kiyono, Anton Popov, Mariia Chernykh and Oleksii Shpenkov
Data 2019, 4(1), 14; https://doi.org/10.3390/data4010014 - 18 Jan 2019
Cited by 147 | Viewed by 19161
Abstract
This work has been carried out to support the investigation of the electroencephalogram (EEG) Fourier power spectral, coherence, and detrended fluctuation characteristics during performance of mental tasks. To this aim, the presented dataset contains International 10/20 system EEG recordings from subjects under mental [...] Read more.
This work has been carried out to support the investigation of the electroencephalogram (EEG) Fourier power spectral, coherence, and detrended fluctuation characteristics during performance of mental tasks. To this aim, the presented dataset contains International 10/20 system EEG recordings from subjects under mental cognitive workload (performing mental serial subtraction) and the corresponding reference background EEGs. Based on the subtraction task performance (number of subtractions and accuracy of the result), the subjects were divided into good counters and bad counters (for whom the mental task required excessive efforts). The data was recorded from 36 healthy volunteers of matched age, all of whom are students of Educational and Scientific Centre “Institute of Biology and Medicine”, National Taras Shevchenko University of Kyiv (Ukraine); the recordings are available through Physiobank platform. The dataset can be used by the neuroscience research community studying brain dynamics during cognitive workload. Full article
(This article belongs to the Special Issue Big Data and Digital Health)
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