BioMedInformatics

Editor's Choices Series for Clinical Informatics Section

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Special Issue Information
Keywords
Published Papers

A special issue of BioMedInformatics (ISSN 2673-7426). This special issue belongs to the section "Clinical Informatics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 928

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Special Issue Information

Dear Colleagues,

The Editor's Choice Series for Clinical Informatics Section presents an exclusive compilation spotlighting innovative methodologies in the realm of clinical informatics. This curated series features diverse methodologies, tools, and approaches revolutionizing the integration of information technology in healthcare, fostering advancements in patient care, data management, and healthcare system optimization.

Encompassing fields such as electronic health records, health information systems, interoperability, decision support systems, and telemedicine, this series offers an in-depth exploration of methodologies driving transformative changes in clinical practice. From sophisticated algorithms enhancing diagnostics to the implementation of secure data sharing frameworks, these articles elucidate the pivotal role of informatics in enhancing healthcare outcomes.

Please note, this series prioritizes comprehensive studies and critical analyses of methodologies and their practical applications in clinical informatics. Submissions for brief reports are not considered for this series.

Dr. José Machado
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. BioMedInformatics is an international peer-reviewed open access quarterly 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 1000 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

electronic health records
health information systems
interoperability
decision support systems
telemedicine
healthcare technology
diagnostics algorithms
data sharing frameworks
healthcare optimization
informatics integration

Published Papers (2 papers)

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Research

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12 pages, 4488 KiB

Open AccessArticle

A Comprehensive Analysis of Trapezius Muscle EMG Activity in Relation to Stress and Meditation

by Mohammad Ahmed, Michael Grillo, Amirtaha Taebi, Mehmet Kaya and Peshala Thibbotuwawa Gamage

BioMedInformatics 2024, 4(2), 1047-1058; https://doi.org/10.3390/biomedinformatics4020058 - 9 Apr 2024

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Abstract

Introduction: This study analyzes the efficacy of trapezius muscle electromyography (EMG) in discerning mental states, namely stress and meditation. Methods: Fifteen healthy participants were monitored to assess their physiological responses to mental stressors and meditation. Sensors were affixed to both the right and left trapezius muscles to capture EMG signals, while simultaneous electroencephalography (EEG) was conducted to validate cognitive states. Results: Our analysis of various EMG features, considering frequency ranges and sensor positioning, revealed significant changes in trapezius muscle activity during stress and meditation. Notably, low-frequency EMG features facilitated enhanced stress detection. For accurate stress identification, sensor configurations can be limited to the right trapezius muscle. Furthermore, the introduction of a novel method for determining asymmetry in EMG features suggests that applying sensors on bilateral trapezius muscles can improve the detection of mental states. Conclusion: This research presents a promising avenue for efficient cognitive state monitoring through compact and convenient sensing. Full article

(This article belongs to the Special Issue Editor's Choices Series for Clinical Informatics Section)

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Review

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19 pages, 784 KiB

Open AccessReview

A Comprehensive Review of the Impact of Machine Learning and Omics on Rare Neurological Diseases

by Nofe Alganmi

BioMedInformatics 2024, 4(2), 1329-1347; https://doi.org/10.3390/biomedinformatics4020073 - 16 May 2024

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Abstract

Background: Rare diseases, predominantly caused by genetic factors and often presenting neurological manifestations, are significantly underrepresented in research. This review addresses the urgent need for advanced research in rare neurological diseases (RNDs), which suffer from a data scarcity and diagnostic challenges. Bridging the gap in RND research is the integration of machine learning (ML) and omics technologies, offering potential insights into the genetic and molecular complexities of these conditions. Methods: We employed a structured search strategy, using a combination of machine learning and omics-related keywords, alongside the names and synonyms of 1840 RNDs as identified by Orphanet. Our inclusion criteria were limited to English language articles that utilized specific ML algorithms in the analysis of omics data related to RNDs. We excluded reviews and animal studies, focusing solely on studies with the clear application of ML in omics data to ensure the relevance and specificity of our research corpus. Results: The structured search revealed the growing use of machine learning algorithms for the discovery of biomarkers and diagnosis of rare neurological diseases (RNDs), with a primary focus on genomics and radiomics because genetic factors and imaging techniques play a crucial role in determining the severity of these diseases. With AI, we can improve diagnosis and mutation detection and develop personalized treatment plans. There are, however, several challenges, including small sample sizes, data heterogeneity, model interpretability, and the need for external validation studies. Conclusions: The sparse knowledge of valid biomarkers, disease pathogenesis, and treatments for rare diseases presents a significant challenge for RND research. The integration of omics and machine learning technologies, coupled with collaboration among stakeholders, is essential to develop personalized treatment plans and improve patient outcomes in this critical medical domain. Full article

(This article belongs to the Special Issue Editor's Choices Series for Clinical Informatics Section)

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