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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: closed (31 March 2025) | Viewed by 7167

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

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

Published Papers (4 papers)

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Research

20 pages, 6079 KiB  
Article
GCBRGCN: Integration of ceRNA and RGCN to Identify Gastric Cancer Biomarkers
by Peng Zhi, Yue Liu, Chenghui Zhao and Kunlun He
Bioengineering 2025, 12(3), 255; https://doi.org/10.3390/bioengineering12030255 - 3 Mar 2025
Viewed by 706
Abstract
Gastric cancer (GC) is a prevalent malignancy, and the discovery of biomarkers plays a crucial role in the diagnosis and prognosis of GC. However, current strategies for identifying GC biomarkers often focus on a single ribonucleic acid (RNA) class, neglecting the potential for [...] Read more.
Gastric cancer (GC) is a prevalent malignancy, and the discovery of biomarkers plays a crucial role in the diagnosis and prognosis of GC. However, current strategies for identifying GC biomarkers often focus on a single ribonucleic acid (RNA) class, neglecting the potential for multiple RNA types to collectively serve as biomarkers with improved predictive capabilities. To bridge this gap, our study introduces the GC biomarker relation graph convolution neural network (GCBRGCN) model which integrates the competing endogenous RNA (ceRNA) network with GC clinical informations and whole transcriptomics data, leveraging the relational graph convolutional network (RGCN) to predict GC biomarkers. It demonstrates exceptional performance, surpassing traditional machine learning and graph neural network algorithms with an area under the curve (AUC) of 0.8172 in the task of predicting GC biomarkers. Our study identified three unreported potential novel GC biomarkers: CCNG1, CYP1B1, and CITED2. Moreover, FOXC1 and LINC00324 were characterized as biomarkers with significance in both prognosis and diagnosis. Our work offers a novel framework for GC biomarker identification, highlighting the critical role of multiple types RNA interaction in oncological research. Full article
(This article belongs to the Special Issue Machine Learning Technology in Predictive Healthcare)
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20 pages, 2847 KiB  
Article
A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence
by Anh T. Tran, Dmitriy Desser, Tal Zeevi, Gaby Abou Karam, Fiona Dierksen, Andrea Dell’Orco, Helge Kniep, Uta Hanning, Jens Fiehler, Julia Zietz, Pina C. Sanelli, Ajay Malhotra, James S. Duncan, Sanjay Aneja, Guido J. Falcone, Adnan I. Qureshi, Kevin N. Sheth, Jawed Nawabi and Seyedmehdi Payabvash
Bioengineering 2024, 11(12), 1274; https://doi.org/10.3390/bioengineering11121274 - 15 Dec 2024
Viewed by 1225
Abstract
Intracerebral hemorrhage (ICH) and perihematomal edema (PHE) are key imaging markers of primary and secondary brain injury in hemorrhagic stroke. Accurate segmentation and quantification of ICH and PHE can help with prognostication and guide treatment planning. In this study, we combined Swin-Unet Transformers [...] Read more.
Intracerebral hemorrhage (ICH) and perihematomal edema (PHE) are key imaging markers of primary and secondary brain injury in hemorrhagic stroke. Accurate segmentation and quantification of ICH and PHE can help with prognostication and guide treatment planning. In this study, we combined Swin-Unet Transformers with nnU-NETv2 convolutional network for segmentation of ICH and PHE on non-contrast head CTs. We also applied test-time data augmentations to assess individual-level prediction uncertainty, ensuring high confidence in prediction. The model was trained on 1782 CT scans from a multicentric trial and tested in two independent datasets from Yale (n = 396) and University of Berlin Charité Hospital and University Medical Center Hamburg-Eppendorf (n = 943). Model performance was evaluated with the Dice coefficient and Volume Similarity (VS). Our dual Swin-nnUNET model achieved a median (95% confidence interval) Dice = 0.93 (0.90–0.95) and VS = 0.97 (0.95–0.98) for ICH, and Dice = 0.70 (0.64–0.75) and VS = 0.87 (0.80–0.93) for PHE segmentation in the Yale cohort. Dice = 0.86 (0.80–0.90) and VS = 0.91 (0.85–0.95) for ICH and Dice = 0.65 (0.56–0.70) and VS = 0.86 (0.77–0.93) for PHE segmentation in the Berlin/Hamburg-Eppendorf cohort. Prediction uncertainty was associated with lower segmentation accuracy, smaller ICH/PHE volumes, and infratentorial location. Our results highlight the benefits of a dual transformer-convolutional neural network architecture for ICH/PHE segmentation and test-time augmentation for uncertainty quantification. Full article
(This article belongs to the Special Issue Machine Learning Technology in Predictive Healthcare)
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22 pages, 2550 KiB  
Article
Ensemble Fusion Models Using Various Strategies and Machine Learning for EEG Classification
by Sunil Kumar Prabhakar, Jae Jun Lee and Dong-Ok Won
Bioengineering 2024, 11(10), 986; https://doi.org/10.3390/bioengineering11100986 - 29 Sep 2024
Cited by 2 | Viewed by 1786
Abstract
Electroencephalography (EEG) helps to assess the electrical activities of the brain so that the neuronal activities of the brain are captured effectively. EEG is used to analyze many neurological disorders, as it serves as a low-cost equipment. To diagnose and treat every neurological [...] Read more.
Electroencephalography (EEG) helps to assess the electrical activities of the brain so that the neuronal activities of the brain are captured effectively. EEG is used to analyze many neurological disorders, as it serves as a low-cost equipment. To diagnose and treat every neurological disorder, lengthy EEG signals are needed, and different machine learning and deep learning techniques have been developed so that the EEG signals could be classified automatically. In this work, five ensemble models are proposed for EEG signal classification, and the main neurological disorder analyzed in this paper is epilepsy. The first proposed ensemble technique utilizes an equidistant assessment and ranking determination mode with the proposed Enhance the Sum of Connection and Distance (ESCD)-based feature selection technique for the classification of EEG signals; the second proposed ensemble technique utilizes the concept of Infinite Independent Component Analysis (I-ICA) and multiple classifiers with majority voting concept; the third proposed ensemble technique utilizes the concept of Genetic Algorithm (GA)-based feature selection technique and bagging Support Vector Machine (SVM)-based classification model. The fourth proposed ensemble technique utilizes the concept of Hilbert Huang Transform (HHT) and multiple classifiers with GA-based multiparameter optimization, and the fifth proposed ensemble technique utilizes the concept of Factor analysis with Ensemble layer K nearest neighbor (KNN) classifier. The best results are obtained when the Ensemble hybrid model using the equidistant assessment and ranking determination method with the proposed ESCD-based feature selection technique and Support Vector Machine (SVM) classifier is utilized, achieving a classification accuracy of 89.98%. Full article
(This article belongs to the Special Issue Machine Learning Technology in Predictive Healthcare)
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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 1806
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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