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Digital Health and Data Analytics in Public Health
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Digital Health and Data Analytics in Public Health

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (29 December 2023) | Viewed by 17250

Special Issue Editor

Prof. Dr. Weng Kee Wong

E-Mail Website
Guest Editor
Department of Biostatistics, UCLA School of Public Health, University of California Los Angeles, Los Angeles, CA 90095-1772, USA
Interests: public health; statistics; data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to announce a new Special Issue on “Digital Health and Data Analytics in Public Health” in the journal Big Data and Cognitive Computing.

Papers in this Special Issue are expected to provide innovations in one or more phases in data analytics: data collection, data processing, data modeling, and decision making. They can be descriptive, diagnostic, predictive or prescriptive analytics that transform raw data into usable information for making decisions in public health. In particular, some of the topics to be addressed are how data analytics can uncover new relationships from highly scaled sets of data using machine learning, high dimensional inference, computational epidemiology, neutral networks, and advanced algorithms, including nature-inspired metaheuristic algorithms; and how new digital health strategies can enable healthcare providers to share data and use analytics to optimize network and deliver outstanding care, promote health equity, improve the future healthcare of people, and better manage disease progression or the spread of diseases.

Papers that demonstrate new methodology that enables enterprise growth for medical research, improve home healthcare strategies, etc. are also welcome.

Prof. Dr. Weng Kee Wong
Guest Editor

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. Big Data and Cognitive Computing 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 1800 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.

Keywords

  • data analytics
  • public health
  • machine learning
  • advanced algorithms

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Published Papers (2 papers)

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Research

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15 pages, 364 KiB  
Article
Machine Learning Techniques for Chronic Kidney Disease Risk Prediction
by Elias Dritsas and Maria Trigka
Big Data Cogn. Comput. 2022, 6(3), 98; https://doi.org/10.3390/bdcc6030098 - 14 Sep 2022
Cited by 64 | Viewed by 9133
Abstract
Chronic kidney disease (CKD) is a condition characterized by progressive loss of kidney function over time. It describes a clinical entity that causes kidney damage and affects the general health of the human body. Improper diagnosis and treatment of the disease can eventually [...] Read more.
Chronic kidney disease (CKD) is a condition characterized by progressive loss of kidney function over time. It describes a clinical entity that causes kidney damage and affects the general health of the human body. Improper diagnosis and treatment of the disease can eventually lead to end-stage renal disease and ultimately lead to the patient’s death. Machine Learning (ML) techniques have acquired an important role in disease prediction and are a useful tool in the field of medical science. In the present research work, we aim to build efficient tools for predicting CKD occurrence, following an approach which exploits ML techniques. More specifically, first, we apply class balancing in order to tackle the non-uniform distribution of the instances in the two classes, then features ranking and analysis are performed, and finally, several ML models are trained and evaluated based on various performance metrics. The derived results highlighted the Rotation Forest (RotF), which prevailed in relation to compared models with an Area Under the Curve (AUC) of 100%, Precision, Recall, F-Measure and Accuracy equal to 99.2%. Full article
(This article belongs to the Special Issue Digital Health and Data Analytics in Public Health)
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Review

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36 pages, 1674 KiB  
Review
Privacy-Enhancing Digital Contact Tracing with Machine Learning for Pandemic Response: A Comprehensive Review
by Ching-Nam Hang, Yi-Zhen Tsai, Pei-Duo Yu, Jiasi Chen and Chee-Wei Tan
Big Data Cogn. Comput. 2023, 7(2), 108; https://doi.org/10.3390/bdcc7020108 - 1 Jun 2023
Cited by 8 | Viewed by 6823
Abstract
The rapid global spread of the coronavirus disease (COVID-19) has severely impacted daily life worldwide. As potential solutions, various digital contact tracing (DCT) strategies have emerged to mitigate the virus’s spread while maintaining economic and social activities. The computational epidemiology problems of DCT [...] Read more.
The rapid global spread of the coronavirus disease (COVID-19) has severely impacted daily life worldwide. As potential solutions, various digital contact tracing (DCT) strategies have emerged to mitigate the virus’s spread while maintaining economic and social activities. The computational epidemiology problems of DCT often involve parameter optimization through learning processes, making it crucial to understand how to apply machine learning techniques for effective DCT optimization. While numerous research studies on DCT have emerged recently, most existing reviews primarily focus on DCT application design and implementation. This paper offers a comprehensive overview of privacy-preserving machine learning-based DCT in preparation for future pandemics. We propose a new taxonomy to classify existing DCT strategies into forward, backward, and proactive contact tracing. We then categorize several DCT apps developed during the COVID-19 pandemic based on their tracing strategies. Furthermore, we derive three research questions related to computational epidemiology for DCT and provide a detailed description of machine learning techniques to address these problems. We discuss the challenges of learning-based DCT and suggest potential solutions. Additionally, we include a case study demonstrating the review’s insights into the pandemic response. Finally, we summarize the study’s limitations and highlight promising future research directions in DCT. Full article
(This article belongs to the Special Issue Digital Health and Data Analytics in Public Health)
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