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Bioengineering
Special Issues
Machine Learning Technology in Predictive Healthcare
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Machine Learning Technology in Predictive Healthcare

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 1283

Special Issue Editors

Dr. Vida Abedi

E-Mail Website
Guest Editor
Department of Public Health Science, Penn State College of Medicine, Hershey, PA, USA
Interests: machine learning; clinical informatics; healthcare innovation; EHR/EMR mining; natural language processing; complex diseases; outcome prediction; health disparity; machine learning-enabled decision support system; stroke; transient ischemic attack; cerebrovascular medicine
Special Issues, Collections and Topics in MDPI journals
Dr. Alireza Vafaei Sadr

E-Mail Website
Guest Editor
Department of Public Health Science, Penn State College of Medicine, Hershey, PA, USA
Interests: big data; data science; machine learning, artificial intelligence, deep learning; data visualization; anomaly detection; computer vision; federated machine learning

Special Issue Information

Dear Colleagues,

Machine learning, an artificial intelligence technique enabling computers to learn and adapt from experience without explicit programming, has considerably impacted the realms of medicine, health, and healthcare. Additionally, precision medicine, an approach that considers individual genetic, environmental, and lifestyle variations, has also gained prominence.

This Special Issue aims to focus on the convergence between machine learning approaches and precision medicine by providing a platform for researchers to share their knowledge and insights. We seek to feature papers that underscore how the use of machine learning can reduce disparity and improve outcomes for mainstream/minority patient populations in healthcare, addressing areas such as the development and application of machine learning algorithms, and methodologies for innovative healthcare and disease management including drug discovery, disease diagnosis, patient stratification, clinical decision support, etc.

We look forward to your submissions, which we believe will be valuable in revolutionizing medical care and improving patient outcomes.

Dr. Vida Abedi
Dr. Alireza Vafaei Sadr
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. Bioengineering 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 2700 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

  • machine learning
  • artificial intelligence
  • precision medicine
  • precision health
  • predictive modeling
  • medical diagnosis
  • medical prognosis
  • healthcare disparity
  • healthcare innovation
  • EHR/EMR mining
  • smart healthcare systems
  • patient stratification

Published Papers (1 paper)

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Research

16 pages, 1311 KiB  
Article
Hybrid Predictive Machine Learning Model for the Prediction of Immunodominant Peptides of Respiratory Syncytial Virus
by Syed Nisar Hussain Bukhari and Kingsley A. Ogudo
Bioengineering 2024, 11(8), 791; https://doi.org/10.3390/bioengineering11080791 - 5 Aug 2024
Viewed by 446
Abstract
Respiratory syncytial virus (RSV) is a common respiratory pathogen that infects the human lungs and respiratory tract, often causing symptoms similar to the common cold. Vaccination is the most effective strategy for managing viral outbreaks. Currently, extensive efforts are focused on developing a [...] Read more.
Respiratory syncytial virus (RSV) is a common respiratory pathogen that infects the human lungs and respiratory tract, often causing symptoms similar to the common cold. Vaccination is the most effective strategy for managing viral outbreaks. Currently, extensive efforts are focused on developing a vaccine for RSV. Traditional vaccine design typically involves using an attenuated form of the pathogen to elicit an immune response. In contrast, peptide-based vaccines (PBVs) aim to identify and chemically synthesize specific immunodominant peptides (IPs), known as T-cell epitopes (TCEs), to induce a targeted immune response. Despite their potential for enhancing vaccine safety and immunogenicity, PBVs have received comparatively less attention. Identifying IPs for PBV design through conventional wet-lab experiments is challenging, costly, and time-consuming. Machine learning (ML) techniques offer a promising alternative, accurately predicting TCEs and significantly reducing the time and cost of vaccine development. This study proposes the development and evaluation of eight hybrid ML predictive models created through the permutations and combinations of two classification methods, two feature weighting techniques, and two feature selection algorithms, all aimed at predicting the TCEs of RSV. The models were trained using the experimentally determined TCEs and non-TCE sequences acquired from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) repository. The hybrid model composed of the XGBoost (XGB) classifier, chi-squared (ChST) weighting technique, and backward search (BST) as the optimal feature selection algorithm (ChST−BST–XGB) was identified as the best model, achieving an accuracy, sensitivity, specificity, F1 score, AUC, precision, and MCC of 97.10%, 0.98, 0.97, 0.98, 0.99, 0.99, and 0.96, respectively. Additionally, K-fold cross-validation (KFCV) was performed to ensure the model’s reliability and an average accuracy of 97.21% was recorded for the ChST−BST–XGB model. The results indicate that the hybrid XGBoost model consistently outperforms other hybrid approaches. The epitopes predicted by the proposed model may serve as promising vaccine candidates for RSV, subject to in vitro and in vivo scientific assessments. This model can assist the scientific community in expediting the screening of active TCE candidates for RSV, ultimately saving time and resources in vaccine development. Full article
(This article belongs to the Special Issue Machine Learning Technology in Predictive Healthcare)
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