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

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 3789

Special Issue Editors

Dr. Haifeng Wang

E-Mail Website
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

E-Mail Website
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

Published Papers (3 papers)

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Research

16 pages, 3162 KiB  
Article
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
Viewed by 478
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 [...] Read more.
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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21 pages, 2771 KiB  
Article
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
Viewed by 817
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 [...] Read more.
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  
Article
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
Viewed by 1857
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 [...] Read more.
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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