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From Data to Diagnosis: Recent Advances of Machine Learning in Biomedical and Health Informatics

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A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 14361

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Special Issue Editors

Dr. Haifeng Wang

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Guest Editor

Department of Industrial and Systems Engineering, Bagley College of Engineering, Mississippi State University, Mississippi State, MS 39762, USA
Interests: deep neural networks; interpretable machine learning; domain adaptation; nonlinear programming; computer-aided diagnosis; medical image analysis; neurological disorder diagnosis; precision agriculture
Special Issues, Collections and Topics in MDPI journals

Dr. Norma B. Ojeda

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Guest Editor

Department of Advanced Biomedical Education, University of Mississippi Medical Center, Jackson, MS 39216, USA
Interests: research on education; quantitative research; behavioral research; immersive technology in biomedical education; machine learning; AI; predictive models applied to biomedical education and healthcare delivery

Dr. Lu He

E-Mail Website
Guest Editor

Marketing, Quantitative Analysis, and Business Law, Mississippi State University, Mississippi State, MS 39762, USA
Interests: scheduling optimization; decision support systems; healthcare; process improvement; machine learning; strategic planning

Special Issue Information

Dear Colleagues,

We are excited to introduce an upcoming Special Issue focused on machine learning in biomedical and health informatics. This Special Issue spotlights the profound potential lying at the intersection of advanced machine learning techniques and biomedical and clinical data.

In the ever-evolving landscape of biomedical and health informatics, the convergence of robust machine learning algorithms with biomedical and clinical data has enabled precision medicine to transcend conceptual boundaries and become an integral part of clinical practice. This Special Issue will gather cutting-edge research that showcases the successful translation of machine learning models into actionable healthcare solutions.

We invite contributions from researchers and practitioners who are at the forefront of this dynamic field. Topics of interest include the following:

Novel machine learning algorithms for medical image analysis and interpretation;
Predictive modeling for disease diagnosis, prognosis, and treatment response;
Integration of biomedical or clinical data for holistic health insights;
Data-driven approaches to drug discovery and repurposing;
Remote monitoring and telehealth solutions;
Ethical considerations and interpretability in machine learning-driven healthcare.

This Special Issue aims to curate impactful research that bridges the gap between data-driven discovery and clinical implementation. We encourage submissions that not only showcase the state-of-the-art but also illuminate the path toward a future where data, technology, and healthcare converge seamlessly. Join us in shaping the discourse at the intersection of machine learning and healthcare. Your contributions have the potential to reshape medical practice and enhance patient well-being.

Dr. Haifeng Wang
Dr. Norma B. Ojeda
Dr. Lu He
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. Information 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 1600 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
biomedical informatics
health informatics
precision medicine
predictive modeling
drug discovery
image processing
telehealth solutions

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Published Papers (7 papers)

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Research

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13 pages, 3105 KiB

Open AccessArticle

AI-Based Detection of Optical Microscopic Images of Pseudomonas aeruginosa in Planktonic and Biofilm States

by Bidisha Sengupta, Mousa Alrubayan, Manideep Kolla, Yibin Wang, Esther Mallet, Angel Torres, Ravyn Solis, Haifeng Wang and Prabhakar Pradhan

Information 2025, 16(4), 309; https://doi.org/10.3390/info16040309 - 14 Apr 2025

Viewed by 326

Abstract

Biofilms are resistant microbial cell aggregates that pose risks to the health and food industries and produce environmental contamination. The accurate and efficient detection and prevention of biofilms are challenging and demand interdisciplinary approaches. This multidisciplinary research reports the application of a deep learning-based artificial intelligence (AI) model for detecting biofilms produced by Pseudomonas aeruginosa with high accuracy. Aptamer DNA-templated silver nanocluster (Ag-NC) was used to prevent biofilm formation, which produced images of the planktonic states of the bacteria. Large-volume bright-field images of bacterial biofilms were used to design the AI model. In particular, we used U-Net with ResNet encoder enhancement to segment biofilm images for AI analysis. Different degrees of biofilm structures can be efficiently detected using ResNet18 and ResNet34 backbones. The potential applications of this technique are also discussed. Full article

(This article belongs to the Special Issue From Data to Diagnosis: Recent Advances of Machine Learning in Biomedical and Health Informatics)

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22 pages, 11597 KiB

Open AccessArticle

MRI Super-Resolution Analysis via MRISR: Deep Learning for Low-Field Imaging

by Yunhe Li, Mei Yang, Tao Bian and Haitao Wu

Information 2024, 15(10), 655; https://doi.org/10.3390/info15100655 - 19 Oct 2024

Viewed by 1958

Abstract

This paper presents a novel MRI super-resolution analysis model, MRISR. Through the utilization of generative adversarial networks for the estimation of degradation kernels and the injection of noise, we have constructed a comprehensive dataset of high-quality paired high- and low-resolution MRI images. The MRISR model seamlessly integrates VMamba and Transformer technologies, demonstrating superior performance across various no-reference image quality assessment metrics compared with existing methodologies. It effectively reconstructs high-resolution MRI images while meticulously preserving intricate texture details, achieving a fourfold enhancement in resolution. This research endeavor represents a significant advancement in the field of MRI super-resolution analysis, contributing a cost-effective solution for rapid MRI technology that holds immense promise for widespread adoption in clinical diagnostic applications. Full article

(This article belongs to the Special Issue From Data to Diagnosis: Recent Advances of Machine Learning in Biomedical and Health Informatics)

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16 pages, 3993 KiB

Open AccessArticle

Computational Analysis of Marker Genes in Alzheimer’s Disease across Multiple Brain Regions

by Panagiotis Karanikolaos, Marios G. Krokidis, Themis P. Exarchos and Panagiotis Vlamos

Information 2024, 15(9), 523; https://doi.org/10.3390/info15090523 - 27 Aug 2024

Viewed by 1694

Abstract

Alzheimer’s disease (AD) is the most common cause of neurodegenerative dementia in the elderly, which is characterized by progressive cognitive impairment. Herein, we undertake a sophisticated computational analysis by integrating single-cell RNA sequencing (scRNA-seq) data from multiple brain regions significantly affected by the disease, including the entorhinal cortex, prefrontal cortex, superior frontal gyrus, and superior parietal lobe. Our pipeline combines datasets derived from the aforementioned tissues into a unified analysis framework, facilitating cross-regional comparisons to provide a holistic view of the impact of the disease on the cellular and molecular landscape of the brain. We employed advanced computational techniques such as batch effect correction, normalization, dimensionality reduction, clustering, and visualization to explore cellular heterogeneity and gene expression patterns across these regions. Our findings suggest that enabling the integration of data from multiple batches can significantly enhance our understanding of AD complexity, thereby identifying key molecular targets for potential therapeutic intervention. This study established a precedent for future research by demonstrating how existing data can be reanalysed in a coherent manner to elucidate the systemic nature of the disease and inform the development of more effective diagnostic tools and targeted therapies. Full article

(This article belongs to the Special Issue From Data to Diagnosis: Recent Advances of Machine Learning in Biomedical and Health Informatics)

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16 pages, 3162 KiB

Open AccessArticle

Utilizing Machine Learning for Context-Aware Digital Biomarker of Stress in Older Adults

by Md Saif Hassan Onim, Himanshu Thapliyal and Elizabeth K. Rhodus

Information 2024, 15(5), 274; https://doi.org/10.3390/info15050274 - 12 May 2024

Cited by 6 | Viewed by 2081

Abstract

Identifying stress in older adults is a crucial field of research in health and well-being. This allows us to take timely preventive measures that can help save lives. That is why a nonobtrusive way of accurate and precise stress detection is necessary. Researchers have proposed many statistical measurements to associate stress with sensor readings from digital biomarkers. With the recent progress of Artificial Intelligence in the healthcare domain, the application of machine learning is showing promising results in stress detection. Still, the viability of machine learning for digital biomarkers of stress is under-explored. In this work, we first investigate the performance of a supervised machine learning algorithm (Random Forest) with manual feature engineering for stress detection with contextual information. The concentration of salivary cortisol was used as the golden standard here. Our framework categorizes stress into No Stress, Low Stress, and High Stress by analyzing digital biomarkers gathered from wearable sensors. We also provide a thorough knowledge of stress in older adults by combining physiological data obtained from wearable sensors with contextual clues from a stress protocol. Our context-aware machine learning model, using sensor fusion, achieved a macroaverage F-1 score of 0.937 and an accuracy of 92.48% in identifying three stress levels. We further extend our work to get rid of the burden of manual feature engineering. We explore Convolutional Neural Network (CNN)-based feature encoder and cortisol biomarkers to detect stress using contextual information. We provide an in-depth look at the CNN-based feature encoder, which effectively separates useful features from physiological inputs. Both of our proposed frameworks, i.e., Random Forest with engineered features and a Fully Connected Network with CNN-based features validate that the integration of digital biomarkers of stress can provide more insight into the stress response even without any self-reporting or caregiver labels. Our method with sensor fusion shows an accuracy and F-1 score of 83.7797% and 0.7552, respectively, without context and 96.7525% accuracy and 0.9745 F-1 score with context, which also constitutes a 4% increase in accuracy and a 0.04 increase in F-1 score from RF. Full article

(This article belongs to the Special Issue From Data to Diagnosis: Recent Advances of Machine Learning in Biomedical and Health Informatics)

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20 pages, 2771 KiB

Open AccessArticle

Advancing Sustainable COVID-19 Diagnosis: Integrating Artificial Intelligence with Bioinformatics in Chest X-ray Analysis

by Hassen Louati, Ali Louati, Rahma Lahyani, Elham Kariri and Abdullah Albanyan

Information 2024, 15(4), 189; https://doi.org/10.3390/info15040189 - 29 Mar 2024

Cited by 4 | Viewed by 1740

Abstract

Responding to the critical health crisis triggered by respiratory illnesses, notably COVID-19, this study introduces an innovative and resource-conscious methodology for analyzing chest X-ray images. We unveil a cutting-edge technique that marries neural architecture search (NAS) with genetic algorithms (GA), aiming to refine the architecture of convolutional neural networks (CNNs) in a way that diminishes the usual demand for computational power. Leveraging transfer learning (TL), our approach efficiently navigates the hurdles posed by scarce data, optimizing both time and hardware utilization—a cornerstone for sustainable AI initiatives. The investigation leverages a curated dataset of 1184 COVID-positive and 1319 COVID-negative chest X-ray images, serving as the basis for model training, evaluation, and validation. Our methodology not only boosts the precision in diagnosing COVID-19 but also establishes a pioneering standard in the realm of eco-friendly and effective healthcare technologies. Through comprehensive comparative analyses against leading-edge models, our optimized solutions exhibit significant performance enhancements alongside a minimized ecological impact. This contribution marks a significant stride towards eco-sustainable medical imaging, presenting a paradigm that prioritizes environmental stewardship while adeptly addressing modern healthcare exigencies. We compare our approach to state-of-the-art architectures through multiple comparative studies. Full article

(This article belongs to the Special Issue From Data to Diagnosis: Recent Advances of Machine Learning in Biomedical and Health Informatics)

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28 pages, 1216 KiB

Open AccessArticle

HaCk: Hand Gesture Classification Using a Convolutional Neural Network and Generative Adversarial Network-Based Data Generation Model

by Kalyan Chatterjee, M. Raju, N. Selvamuthukumaran, M. Pramod, B. Krishna Kumar, Anjan Bandyopadhyay and Saurav Mallik

Information 2024, 15(2), 85; https://doi.org/10.3390/info15020085 - 4 Feb 2024

Cited by 4 | Viewed by 3549

Abstract

According to global data on visual impairment from the World Health Organization in 2010, an estimated 285 million individuals, including 39 million who are blind, face visual impairments. These individuals use non-contact methods such as voice commands and hand gestures to interact with user interfaces. Recognizing the significance of hand gesture recognition for this vulnerable population and aiming to improve user usability, this study employs a Generative Adversarial Network (GAN) coupled with Convolutional Neural Network (CNN) techniques to generate a diverse set of hand gestures. Recognizing hand gestures using HaCk typically involves a two-step approach. First, the GAN is trained to generate synthetic hand gesture images, and then a separate CNN is employed to classify gestures in real-world data. The evaluation of HaCk is demonstrated through a comparative analysis using Leave-One-Out Cross-Validation (LOO CV) and Holdout Cross-Validation (Holdout CV) tests. These tests are crucial for assessing the model’s generalization, robustness, and suitability for practical applications. The experimental results reveal that the performance of HaCk surpasses that of other compared ML/DL models, including CNN, FTCNN, CDCGAN, GestureGAN, GGAN, MHG-CAN, and ASL models. Specifically, the improvement percentages for the LOO CV Test are 17.03%, 20.27%, 15.76%, 13.76%, 10.16%, 5.90%, and 15.90%, respectively. Similarly, for the Holdout CV Test, HaCk outperforms HU, ZM, GB, GB-ZM, GB-HU, CDCGAN, GestureGAN, GGAN, MHG-CAN, and ASL models, with improvement percentages of 56.87%, 15.91%, 13.97%, 24.81%, 23.52%, 17.72%, 15.72%, 12.12%, 7.94%, and 17.94%, respectively. Full article

(This article belongs to the Special Issue From Data to Diagnosis: Recent Advances of Machine Learning in Biomedical and Health Informatics)

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Review

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20 pages, 2538 KiB

Open AccessReview

Multi-Modal Fusion of Routine Care Electronic Health Records (EHR): A Scoping Review

by Zina Ben-Miled, Jacob A. Shebesh, Jing Su, Paul R. Dexter, Randall W. Grout and Malaz A. Boustani

Information 2025, 16(1), 54; https://doi.org/10.3390/info16010054 - 15 Jan 2025

Viewed by 1649

Abstract

Background: Electronic health records (EHR) are now widely available in healthcare institutions to document the medical history of patients as they interact with healthcare services. In particular, routine care EHR data are collected for a large number of patients.These data span multiple heterogeneous elements (i.e., demographics, diagnosis, medications, clinical notes, vital signs, and laboratory results) which contain semantic, concept, and temporal information. Recent advances in generative learning techniques were able to leverage the fusion of multiple routine care EHR data elements to enhance clinical decision support. Objective: A scoping review of the proposed techniques including fusion architectures, input data elements, and application areas is needed to synthesize variances and identify research gaps that can promote re-use of these techniques for new clinical outcomes. Design: A comprehensive literature search was conducted using Google Scholar to identify high impact fusion architectures over multi-modal routine care EHR data during the period 2018 to 2023. The guidelines from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping review were followed. The findings were derived from the selected studies using a thematic and comparative analysis. Results: The scoping review revealed the lack of standard definition for EHR data elements as they are transformed into input modalities. These definitions ignore one or more key characteristics of the data including source, encoding scheme, and concept level. Moreover, in order to adapt to emergent generative learning techniques, the classification of fusion architectures should distinguish fusion from learning and take into consideration that learning can concurrently happen in all three layers of new fusion architectures (i.e., encoding, representation, and decision). These aspects constitute the first step towards a streamlined approach to the design of multi-modal fusion architectures for routine care EHR data. In addition, current pretrained encoding models are inconsistent in their handling of temporal and semantic information thereby hindering their re-use for different applications and clinical settings. Conclusions: Current routine care EHR fusion architectures mostly follow a design-by-example methodology. Guidelines are needed for the design of efficient multi-modal models for a broad range of healthcare applications. In addition to promoting re-use, these guidelines need to outline best practices for combining multiple modalities while leveraging transfer learning and co-learning as well as semantic and temporal encoding. Full article

(This article belongs to the Special Issue From Data to Diagnosis: Recent Advances of Machine Learning in Biomedical and Health Informatics)

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