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Electronics
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Data Analytics and Visualization in Health Informatics
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Data Analytics and Visualization in Health Informatics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 8181

Special Issue Editors

Dr. Ashad Kabir

E-Mail Website
Guest Editor
Data Science Research Unit, School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2795, Australia
Interests: health informatics; smartphone applications; machine learning; data mining; misinformation; visualization; human computer interaction
Dr. Shariful Islam

E-Mail Website
Guest Editor
Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Faculty of Health, Deakin University, Burwood, VIC 3125, Australia
Interests: digital health; wearables sensors; health informatics; mHealth; machine learning; big data
Dr. Enamul Hoque

E-Mail Website
Guest Editor
School of Information Technology, York University, Toronto, ON M3J 1P3, Canada
Interests: information visualization; human–computer interaction; natural language processing
Dr. Mufti Mahmud

E-Mail Website
Guest Editor
School of Science and Technology, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK
Interests: brain informatics; data analytics; brain–machine interfacing; Internet of healthcare things
Special Issues, Collections and Topics in MDPI journals
Dr. Naeemul Hassan

E-Mail Website
Guest Editor
1. Philip Merrill College of Journalism, University of Maryland, College Park, MD 20742, USA
2. College of Information Studies, University of Maryland, College Park, MD 20742, USA
Interests: computational journalism; natural language processing; data mining

Special Issue Information

Dear Colleagues,

Health informatics (HI) has emerged as a growing domain of interest among researchers worldwide, owing to its significant implications on society. It aims to develop methods and technologies for acquiring, processing, detecting, and visualizing health-related data that can come from different sources and modalities, such as electronic health records, diagnostic test results, medical scans, news articles, etc. Making sense of all this information is a challenging process.      

Artificial Intelligence (AI) and Machine Learning (ML) use algorithms to learn from data to gain knowledge from experience and make decisions and predictions. Information visualizations (IV) are interactive visual externalizations of abstract data that can assist one in making sense of a large amount of information. Thus, applying AI, ML, and IV in health informatics has the most significant potential to raise the quality, efficacy, and efficiency of treatment and care.

The purpose of this Special Issue is to seek high-quality submissions that highlight emerging applications of information visualization, artificial intelligence, and data analytics techniques for health informatics.

Topics of interest include, but are not limited to, the following: 

  • Interactive visualization for health informatics;
  • Robust visual encoding methods for health informatics;
  • Cognitive and perception science for health data visualization;
  • Explainable visualization system for health informatics;
  • Machine learning for health informatics;
  • Artificial Intelligence for health informatics;
  • Data Mining and Knowledge discovery for health informatics;
  • Health-related misinformation/fake news detection;
  • COVID-19 data analysis and visualization;
  • Health-related smartphone apps use;
  • Smartphone apps development for health;
  • Wearable devices and sensors for health;
  • Artificial intelligence for behavior data analysis.

Dr. Ashad Kabir
Dr. Sheikh Mohammed Shariful Islam
Dr. Enamul Hoque
Dr. Mufti Mahmud
Dr. Naeemul Hassan
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • health informatics
  • visualizing health data
  • interactive visualization for health care
  • machine learning for health data
  • artificial intelligence for health data
  • data mining for health data
  • health-related misinformation/fake news detection
  • COVID-19 data analysis and visualization
  • health-related smartphone apps
  • mobile health informatics
  • wearables and sensors
  • explainable AI for visual interactive system
  • explainable data analysis

Published Papers (4 papers)

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Research

32 pages, 8946 KiB  
Article
SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization
by Nuruzzaman Faruqui, Mohammad Abu Yousuf, Md Whaiduzzaman, AKM Azad, Salem A. Alyami, Pietro Liò, Muhammad Ashad Kabir and Mohammad Ali Moni
Electronics 2023, 12(17), 3541; https://doi.org/10.3390/electronics12173541 - 22 Aug 2023
Cited by 15 | Viewed by 1900
Abstract
The Internet of Medical Things (IoMT) has become an attractive playground to cybercriminals because of its market worth and rapid growth. These devices have limited computational capabilities, which ensure minimum power absorption. Moreover, the manufacturers use simplified architecture to offer a competitive price [...] Read more.
The Internet of Medical Things (IoMT) has become an attractive playground to cybercriminals because of its market worth and rapid growth. These devices have limited computational capabilities, which ensure minimum power absorption. Moreover, the manufacturers use simplified architecture to offer a competitive price in the market. As a result, IoMTs cannot employ advanced security algorithms to defend against cyber-attacks. IoMT has become easy prey for cybercriminals due to its access to valuable data and the rapidly expanding market, as well as being comparatively easier to exploit.As a result, the intrusion rate in IoMT is experiencing a surge. This paper proposes a novel Intrusion Detection System (IDS), namely SafetyMed, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to defend against intrusion from sequential and grid data. SafetyMed is the first IDS that protects IoMT devices from malicious image data and sequential network traffic. This innovative IDS ensures an optimized detection rate by trade-off between False Positive Rate (FPR) and Detection Rate (DR). It detects intrusions with an average accuracy of 97.63% with average precision and recall, and has an F1-score of 98.47%, 97%, and 97.73%, respectively. In summary, SafetyMed has the potential to revolutionize many vulnerable sectors (e.g., medical) by ensuring maximum protection against IoMT intrusion. Full article
(This article belongs to the Special Issue Data Analytics and Visualization in Health Informatics)
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17 pages, 1658 KiB  
Article
Artifact Detection in Lung Ultrasound: An Analytical Approach
by Maroš Hliboký, Ján Magyar, Marek Bundzel, Marek Malík, Martin Števík, Štefánia Vetešková, Anton Dzian, Martina Szabóová and František Babič
Electronics 2023, 12(7), 1551; https://doi.org/10.3390/electronics12071551 - 25 Mar 2023
Cited by 1 | Viewed by 1430
Abstract
Lung ultrasound is used to detect various artifacts in the lungs that support the diagnosis of different conditions. There is ongoing research to support the automatic detection of such artifacts using machine learning. We propose a solution that uses analytical computer vision methods [...] Read more.
Lung ultrasound is used to detect various artifacts in the lungs that support the diagnosis of different conditions. There is ongoing research to support the automatic detection of such artifacts using machine learning. We propose a solution that uses analytical computer vision methods to detect two types of lung artifacts, namely A- and B-lines. We evaluate the proposed approach on the POCUS dataset and data acquired from a hospital. We show that by using the Fourier transform, we can analyze lung ultrasound images in real-time and classify videos with an accuracy above 70%. We also evaluate the method’s applicability for segmentation, showcasing its high success rate for B-lines (89% accuracy) and its shortcomings for A-line detection. We then propose a hybrid solution that uses a combination of neural networks and analytical methods to increase accuracy in horizontal line detection, emphasizing the pleura. Full article
(This article belongs to the Special Issue Data Analytics and Visualization in Health Informatics)
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12 pages, 1849 KiB  
Article
Development of an Android Mobile Application for Reducing Sitting Time and Increasing Walking Time in People with Type 2 Diabetes
by Reza Daryabeygi-Khotbehsara, Sheikh Mohammed Shariful Islam, David W. Dunstan, Mohamed Abdelrazek, Brittany Markides, Thien Pham and Ralph Maddison
Electronics 2022, 11(19), 3011; https://doi.org/10.3390/electronics11193011 - 22 Sep 2022
Cited by 3 | Viewed by 1734
Abstract
Breaking up prolonged sitting with short bouts of light physical activity including standing and walking has been shown to be beneficial for people with type 2 diabetes (T2D). This paper presents the development of an android mobile app to deliver a just-in-time adaptive [...] Read more.
Breaking up prolonged sitting with short bouts of light physical activity including standing and walking has been shown to be beneficial for people with type 2 diabetes (T2D). This paper presents the development of an android mobile app to deliver a just-in-time adaptive intervention (JITAI) to reduce sedentary time in people with T2D. A total of six design workshops were conducted with seven experts to identify design requirements, a behavioural framework, and required contextual adaptations for the development of a bespoke mobile app (iMOVE). Moreover, a focus group was conducted among people with T2D as potential end-users (N = 10) to ascertain their perceptions of the app. Feedback from the focus group was used in subsequent iterations of the iMOVE app. Data were analysed using an inductive qualitative thematic analysis. Based on workshops, key features of iMOVE were developed, including simplicity (e.g., navigation, login), colours and font sizes, push notifications, messaging algorithms, and a triggering system for breaking up sitting time and moving more. Based on the user testing results, a goal-setting tab was added, font sizes were made larger, the brightness of colours was reduced, and a colour indicator was used to indicate device connectivity with an activity tracker. A user-centric app was developed to support people with T2D to transition from sedentary to active lifestyles. Full article
(This article belongs to the Special Issue Data Analytics and Visualization in Health Informatics)
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22 pages, 1929 KiB  
Article
Understanding Frailty: Probabilistic Causality between Components and Their Relationship with Death through a Bayesian Network and Evidence Propagation
by Ricardo Ramírez-Aldana, Juan Carlos Gomez-Verjan, Carmen García-Peña, Luis Miguel Gutiérrez-Robledo and Lorena Parra-Rodríguez
Electronics 2022, 11(19), 3001; https://doi.org/10.3390/electronics11193001 - 22 Sep 2022
Cited by 1 | Viewed by 1167
Abstract
Identifying relationships between components of an index helps to gain a better understanding of the condition they define. The Frailty Index (FI) measures the global health of individuals and can be used to predict outcomes as mortality. Previously, we modelled the relationship between [...] Read more.
Identifying relationships between components of an index helps to gain a better understanding of the condition they define. The Frailty Index (FI) measures the global health of individuals and can be used to predict outcomes as mortality. Previously, we modelled the relationship between the FI components (deficits) and death through an undirected graphical model and a social network analysis framework. Here, we model the FI components and death through an averaged Bayesian network obtained through a structural learning process and resampling, in order to understand how the FI components and death are causally related. We identified that components are not similarly related between them and that deficits are related according to their type. Two deficits were the most relevant in terms of their connections, and two others were directly associated with death. We obtained the strength of the relationships in order to identify the most plausible, identifying clusters of deficits. Finally, we propagated evidence and studied how FI components predict mortality, obtaining a correct assignation of almost 74% and a true positive rate (TPR) of 56%. Values were obtained after changing the model threshold (via Youden’s Index maximization) whose possible values are represented in a Receiving Operating Characteristic (ROC) curve (TPR vs. 1-True Negative Rate). The greater number of deficits included for the evidence, the best performances; nevertheless, the FI does not seem to be quite efficient to correctly differentiate between dead and living people. Full article
(This article belongs to the Special Issue Data Analytics and Visualization in Health Informatics)
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