BioMedInformatics

Special Issue Information

Dear Colleagues,

The Editor-in-Chief's Choice in Biomedical Informatics is an esteemed anthology, meticulously curated to highlight the forefront of transformative methodologies in the convergence of biomedical sciences and informatics. This distinguished compendium showcases the pinnacle of innovative techniques, interdisciplinary collaborations, and pioneering approaches that redefine healthcare, research, and technological innovation.

Spanning bioinformatics, computational biology, medical imaging analysis, artificial intelligence, and machine learning, this seminal Issue embodies the intellectual rigor and visionary insights driving informatics in healthcare. Each article serves as a testament to scholarly depth, exploring intricate algorithmic frameworks and groundbreaking applications fostering precision medicine, clinical decision support systems, and predictive healthcare analytics.

Please note, this exclusive collection does not entertain submissions for brief reports. Instead, it presents comprehensive studies and visionary analyses, transcending traditional boundaries and illuminating the pivotal role of informatics in advancing biological understanding, with the goal of transforming healthcare and inspiring process at novel scientific frontiers.

Prof. Dr. Alexandre G. De Brevern
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

bioinformatics
computational biology
medical imaging analysis
artificial intelligence
machine learning
precision medicine
clinical decision support systems
predictive healthcare analytics
transdisciplinary collaboration
technological innovation

Published Papers (2 papers)

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Research

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15 pages, 1079 KiB

Open AccessArticle

Pediatric and Adolescent Seizure Detection: A Machine Learning Approach Exploring the Influence of Age and Sex in Electroencephalogram Analysis

by Lan Wei and Catherine Mooney

BioMedInformatics 2024, 4(1), 796-810; https://doi.org/10.3390/biomedinformatics4010044 - 6 Mar 2024

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Abstract

Background: Epilepsy, a prevalent neurological disorder characterized by recurrent seizures affecting an estimated 70 million people worldwide, poses a significant diagnostic challenge. EEG serves as an important tool in identifying these seizures, but the manual examination of EEGs by experts is time-consuming. To expedite this process, automated seizure detection methods have emerged as powerful aids for expert EEG analysis. It is worth noting that while such methods are well-established for adult EEGs, they have been underdeveloped for pediatric and adolescent EEGs. This study sought to address this gap by devising an automatic seizure detection system tailored for pediatric and adolescent EEG data. Methods: Leveraging publicly available datasets, the TUH pediatric and adolescent EEG and CHB-MIT EEG datasets, the machine learning-based models were constructed. The TUH pediatric and adolescent EEG dataset was divided into training (n = 118), validation (n = 19), and testing (n = 37) subsets, with special attention to ensure a clear demarcation between the individuals in the training and test sets to preserve the test set’s independence. The CHB-MIT EEG dataset was used as an external test set. Age and sex were incorporated as features in the models to investigate their potential influence on seizure detection. Results: By leveraging 20 features extracted from both time and frequency domains, along with age as an additional feature, the method achieved an accuracy of 98.95% on the TUH test set and 64.82% on the CHB-MIT external test set. Our investigation revealed that age is a crucial factor for accurate seizure detection in pediatric and adolescent EEGs. Conclusion: The outcomes of this study hold substantial promise in supporting researchers and clinicians engaged in the automated analysis of seizures in pediatric and adolescent EEGs. Full article

(This article belongs to the Special Issue Editor-in-Chief's Choices in Biomedical Informatics)

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Review

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24 pages, 1113 KiB

Open AccessReview

Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit

by Dimitrios Rallis, Maria Baltogianni, Konstantina Kapetaniou and Vasileios Giapros

BioMedInformatics 2024, 4(2), 1225-1248; https://doi.org/10.3390/biomedinformatics4020067 (registering DOI) - 9 May 2024

Abstract

Artificial intelligence (AI) refers to computer algorithms that replicate the cognitive function of humans. Machine learning is widely applicable using structured and unstructured data, while deep learning is derived from the neural networks of the human brain that process and interpret information. During the last decades, AI has been introduced in several aspects of healthcare. In this review, we aim to present the current application of AI in the neonatal intensive care unit. AI-based models have been applied to neurocritical care, including automated seizure detection algorithms and electroencephalogram-based hypoxic-ischemic encephalopathy severity grading systems. Moreover, AI models evaluating magnetic resonance imaging contributed to the progress of the evaluation of the neonatal developing brain and the understanding of how prenatal events affect both structural and functional network topologies. Furthermore, AI algorithms have been applied to predict the development of bronchopulmonary dysplasia and assess the extubation readiness of preterm neonates. Automated models have been also used for the detection of retinopathy of prematurity and the need for treatment. Among others, AI algorithms have been utilized for the detection of sepsis, the need for patent ductus arteriosus treatment, the evaluation of jaundice, and the detection of gastrointestinal morbidities. Finally, AI prediction models have been constructed for the evaluation of the neurodevelopmental outcome and the overall mortality of neonates. Although the application of AI in neonatology is encouraging, further research in AI models is warranted in the future including retraining clinical trials, validating the outcomes, and addressing serious ethics issues. Full article

(This article belongs to the Special Issue Editor-in-Chief's Choices in Biomedical Informatics)

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