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BioMedInformatics
Special Issues
Editor-in-Chief's Choices in Biomedical Informatics
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Editor-in-Chief's Choices in Biomedical Informatics

A special issue of BioMedInformatics (ISSN 2673-7426).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 6675

Special Issue Editor

Dr. Alexandre G. De Brevern

E-Mail Website
Guest Editor
Department of Biological Research on the Red Blood Cells, INTS, INSERM UMR_S 1134, Université de Paris, Université de la Réunion, 75739 Paris, France
Interests: structural bioinformatics; bioinformatics; next-generation sequence; drug design; deep learning
Special Issues, Collections and Topics in MDPI journals

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

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

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10 pages, 243 KiB  
Article
Association Between Social Determinants of Health and Patient Portal Utilization in the United States
by Elizabeth Ayangunna, Gulzar H. Shah, Hani Samawi, Kristie C. Waterfield and Ana M. Palacios
BioMedInformatics 2024, 4(4), 2213-2222; https://doi.org/10.3390/biomedinformatics4040119 - 12 Nov 2024
Viewed by 570
Abstract
(1) Background: Differences in health outcomes across populations are due to disparities in access to the social determinants of health (SDoH), such as educational level, household income, and internet access. With several positive outcomes reported with patient portal use, examining the associated social [...] Read more.
(1) Background: Differences in health outcomes across populations are due to disparities in access to the social determinants of health (SDoH), such as educational level, household income, and internet access. With several positive outcomes reported with patient portal use, examining the associated social determinants of health is imperative. Objective: This study analyzed the association between social determinants of health—education, health insurance, household income, rurality, and internet access—and patient portal use among adults in the United States before and after the COVID-19 pandemic. (2) Methods: The research used a quantitative, retrospective study design and secondary data from the combined cycles 1 to 4 of the Health Information National Trends Survey 5 (N = 14,103) and 6 (N = 5958). Descriptive statistics and logistic regression were conducted to examine the association between the variables operationalizing SDoH and the use of patient portals. (3) Results: Forty-percent (40%) of respondents reported using a patient portal before the pandemic, and this increased to 61% in 2022. The multivariable logistic regression showed higher odds of patient portal utilization by women compared to men (AOR = 1.56; CI, 1.32–1.83), those with at least a college degree compared to less than high school education (AOR = 2.23; CI, 1.29–3.83), and annual family income of USD 75,000 and above compared to those <USD 20,000 (AOR = 1.59; CI, 1.18–2.15). Those with access to the internet and health insurance also had significantly higher odds of using their patient portals. However, those who identified as Hispanic and non-Hispanic Black and residing in a rural area rather than urban (AOR = 0.72; CI, 0.54–0.95) had significantly lower odds of using their patient portals even after the pandemic. (4) Conclusions: The social determinants of health included in this study showed significant influence on patient portal utilization, which has implications for policymakers and public health stakeholders tasked with promoting patient portal utilization and its benefits. Full article
(This article belongs to the Special Issue Editor-in-Chief's Choices in Biomedical Informatics)
20 pages, 2695 KiB  
Article
Optimizing Lung Condition Categorization through a Deep Learning Approach to Chest X-ray Image Analysis
by Theodora Sanida, Maria Vasiliki Sanida, Argyrios Sideris and Minas Dasygenis
BioMedInformatics 2024, 4(3), 2002-2021; https://doi.org/10.3390/biomedinformatics4030109 - 10 Sep 2024
Viewed by 1089
Abstract
Background: Evaluating chest X-rays is a complex and high-demand task due to the intrinsic challenges associated with diagnosing a wide range of pulmonary conditions. Therefore, advanced methodologies are required to categorize multiple conditions from chest X-ray images accurately. Methods: This study introduces an [...] Read more.
Background: Evaluating chest X-rays is a complex and high-demand task due to the intrinsic challenges associated with diagnosing a wide range of pulmonary conditions. Therefore, advanced methodologies are required to categorize multiple conditions from chest X-ray images accurately. Methods: This study introduces an optimized deep learning approach designed for the multi-label categorization of chest X-ray images, covering a broad spectrum of conditions, including lung opacity, normative pulmonary states, COVID-19, bacterial pneumonia, viral pneumonia, and tuberculosis. An optimized deep learning model based on the modified VGG16 architecture with SE blocks was developed and applied to a large dataset of chest X-ray images. The model was evaluated against state-of-the-art techniques using metrics such as accuracy, F1-score, precision, recall, and area under the curve (AUC). Results: The modified VGG16-SE model demonstrated superior performance across all evaluated metrics. The model achieved an accuracy of 98.49%, an F1-score of 98.23%, a precision of 98.41%, a recall of 98.07% and an AUC of 98.86%. Conclusion: This study provides an effective deep learning approach for categorizing chest X-rays. The model’s high performance across various lung conditions suggests its potential for integration into clinical workflows, enhancing the accuracy and speed of pulmonary disease diagnosis. Full article
(This article belongs to the Special Issue Editor-in-Chief's Choices in Biomedical Informatics)
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15 pages, 1079 KiB  
Article
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
Cited by 1 | Viewed by 1171
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 [...] Read more.
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  
Review
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 - 9 May 2024
Viewed by 3189
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 [...] Read more.
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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