Trends in Electronics and Health Informatics

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 7675

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International Center for Materials and Nanoarchitectronics (MANA), Research Center for Advanced Measurement and Characterization (RCAMC), National Institute for Materials Science (NIMS), 1-2-1 Sengen, Main Bldg, Tsukuba 815, Japan
Interests: bioinformatics; information theory; modelling human brain; dielectric resonance of biomaterials; proteins; neuron; organic jelly-based neuromorphic device; artificial brain; molecular robots for drug delivery
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Dept. of Physics, Amity School of Applied Sciences, Amity University Rajasthan, Kant Kalwar, NH-11C, Jaipur, India
Interests: cognition; antenna; bioelectromagnetics; plasmonics; applied physics; consciousness
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The TEHI (Trends in Electronics and Health Informatics, https://www.tehi.one/) conference series is being developed in view of being the world's premier creative research forum on electronics and health informatics. This is an emerging interdisciplinary and multidisciplinary research field comprising combined contributions from artificial intelligence and soft computing, healthcare information, IoT and data analytics, electronics and communication technology. The conference is organized by IIOIR (www.iioir.org), and its objective is to bring together researchers, educators, and business professionals involved in related fields of research and development. The associated Special Issue is to be published by Information, a journal published by MDPI that covers the above topics in addition to all special features covered in the TEHI conference series. Interested authors can submit their manuscript directly to Information or via the dedicated website for TEHI, whereby all publications that are part of this conference Special Issue will be entitled to a 30% discount.

Authors of invited papers should be aware that the final submitted manuscript must provide a minimum of 50% new content and not exceed 30% of content directly taken from the Proceedings paper.

Dr. Anirban Bandyopadhyay
Dr. Kanad Ray
Guest Editors

Manuscript Submission Information

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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. Information 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.

Keywords

  • artificial intelligence and soft computing
  • healthcare informatics
  • IoT and data analytics
  • electronics
  • communication

Published Papers (3 papers)

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Research

21 pages, 1433 KiB  
Article
PDD-ET: Parkinson’s Disease Detection Using ML Ensemble Techniques and Customized Big Dataset
by Kalyan Chatterjee, Ramagiri Praveen Kumar, Anjan Bandyopadhyay, Sujata Swain, Saurav Mallik, Aimin Li and Kanad Ray
Information 2023, 14(9), 502; https://doi.org/10.3390/info14090502 - 13 Sep 2023
Cited by 2 | Viewed by 2235
Abstract
Parkinson’s disease (PD) is a neurological disorder affecting the nerve cells. PD gives rise to various neurological conditions, including gradual reduction in movement speed, tremors, limb stiffness, and alterations in walking patterns. Identifying Parkinson’s disease in its initial phases is crucial to preserving [...] Read more.
Parkinson’s disease (PD) is a neurological disorder affecting the nerve cells. PD gives rise to various neurological conditions, including gradual reduction in movement speed, tremors, limb stiffness, and alterations in walking patterns. Identifying Parkinson’s disease in its initial phases is crucial to preserving the well-being of those afflicted. However, accurately identifying PD in its early phases is intricate due to the aging population. Therefore, in this paper, we harnessed machine learning-based ensemble methodologies and focused on the premotor stage of PD to create a precise and reliable early-stage PD detection model named PDD-ET. We compiled a tailored, extensive dataset encompassing patient mobility, medication habits, prior medical history, rigidity, gender, and age group. The PDD-ET model amalgamates the outcomes of various ML techniques, resulting in an impressive 97.52% accuracy in early-stage PD detection. Furthermore, the PDD-ET model effectively distinguishes between multiple stages of PD and accurately categorizes the severity levels of patients affected by PD. The evaluation findings demonstrate that the PDD-ET model outperforms the SVR, CNN, Stacked LSTM, LSTM, GRU, Alex Net, [Decision Tree, RF, and SVR], Deep Neural Network, HOG, Quantum ReLU Activator, Improved KNN, Adaptive Boosting, RF, and Deep Learning Model techniques by the approximate margins of 37%, 30%, 20%, 27%, 25%, 18%, 19%, 27%, 25%, 23%, 45%, 40%, 42%, and 16%, respectively. Full article
(This article belongs to the Special Issue Trends in Electronics and Health Informatics)
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18 pages, 4115 KiB  
Article
Intelligence Amplification-Based Smart Health Record Chain for Enterprise Management System
by S. Velliangiri, P. Karthikeyan, Vinayakumar Ravi, Meshari Almeshari and Yasser Alzamil
Information 2023, 14(5), 284; https://doi.org/10.3390/info14050284 - 11 May 2023
Viewed by 1615
Abstract
Medical service providers generate many healthcare records containing sensitive and private information about a patient’s health. The patient can allow healthcare service providers to generate healthcare data, which can be stored with healthcare service providers. After some time, if the patient wants to [...] Read more.
Medical service providers generate many healthcare records containing sensitive and private information about a patient’s health. The patient can allow healthcare service providers to generate healthcare data, which can be stored with healthcare service providers. After some time, if the patient wants to share the healthcare records of one healthcare service provider with another, we can quickly exchange the healthcare record using our approaches. The challenges faced by healthcare service providers are healthcare record sharing, tampering, and insurance fraud. We have developed Health Record Chain for Sharing Medical Data using the modified SHA-512 algorithm. We have evaluated our methods, and our method outperforms in terms of storage cost and total time consumption for health record sharing. The proposed model takes 130 ms to share 100,000 records, 32 ms faster than traditional methods. It also resists various security attacks, as verified by an automated security protocol verification tool. Full article
(This article belongs to the Special Issue Trends in Electronics and Health Informatics)
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15 pages, 1057 KiB  
Article
Rebuilding Stakeholder Confidence in Health-Relevant Big Data Applications: A Social Representations Perspective
by Anthony M. Maina and Upasana G. Singh
Information 2022, 13(9), 441; https://doi.org/10.3390/info13090441 - 19 Sep 2022
Viewed by 2295
Abstract
Big data applications are at the epicentre of recent breakthroughs in digital health. However, controversies over privacy, security, ethics, accountability, and data governance have tarnished stakeholder trust, leaving health-relevant big data projects under threat, delayed, or abandoned. Taking the notion of big data [...] Read more.
Big data applications are at the epicentre of recent breakthroughs in digital health. However, controversies over privacy, security, ethics, accountability, and data governance have tarnished stakeholder trust, leaving health-relevant big data projects under threat, delayed, or abandoned. Taking the notion of big data as social construction, this work explores the social representations of the big data concept from the perspective of stakeholders in Kenya’s digital health environment. Through analysing the similarities and differences in the way health professionals and information technology (IT) practitioners comprehend the idea of big data, we draw strategic implications for restoring confidence in big data initiatives. Respondents associated big data with a multiplicity of concepts and were conflicted in how they represented big data’s benefits and challenges. On this point, we argue that peculiarities and nuances in how diverse players view big data contribute to the erosion of trust and the need to revamp stakeholder engagement practices. Specifically, decision makers should complement generalised informational campaigns with targeted, differentiated messages designed to address data responsibility, access, control, security, or other issues relevant to a specialised but influential community. Full article
(This article belongs to the Special Issue Trends in Electronics and Health Informatics)
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