Advancements in Signal Processing and Machine Learning for Healthcare

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 1049

Special Issue Editors


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Guest Editor
Department of Computer Science, The University of Sheffield, Sheffield S10 2TN, UK
Interests: health data science; bedside and remote patient monitoring; wearable computing; cardiovascular monitoring

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Guest Editor
School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, Indonesia
Interests: biomedical signal processing; AI for healthcare; wearable and contactless vital sign monitoring; brain-machine interfaces; biomedical circuits and systems
Australian E-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD 4074, Australia
Interests: medical informatics; smart homes; fall detection; sensor technologies for healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research in the development of signal processing and machine learning algorithms to this Special Issue, “Advancements in Signal Processing and Machine Learning for Healthcare”. This Special Issue aims to highlight the latest innovations and breakthroughs at the intersection of signal processing, machine learning, and healthcare, with a particular emphasis on advancing the fields of health monitoring, diagnosis, and personalized care. As the integration of technology into healthcare continues to evolve, signal processing and machine learning techniques play increasingly pivotal roles in extracting meaningful insights from complex healthcare data, ultimately leading to improved patient outcomes and enhanced quality of care. Topics of interest include but are not limited to physiological signal processing, wearable devices, remote monitoring systems, predictive modeling, medical imaging analysis, clinical decision support systems, and personalized medicine. We welcome submissions that address challenges in data acquisition, processing, analysis, interpretation, and decision making, with the overarching goals of advancing state-of-the-art healthcare technology and transforming the delivery of healthcare services.

Dr. Shaoxiong Sun
Dr. Nur Ahmadi
Dr. Wei Lu
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. Algorithms 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

  • biomedical signal processing
  • machine learning
  • wearable devices/computing
  • medical imaging
  • contact/contactless monitoring
  • sensor fusion
  • health informatics
  • remote patient monitoring
  • disease modeling
  • health data science

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Published Papers (1 paper)

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Research

16 pages, 794 KiB  
Article
A Machine Learning Approach to Identifying Risk Factors for Long COVID-19
by Rhea Machado, Reshen Soorinarain Dodhy, Atharve Sehgal, Kate Rattigan, Aparna Lalwani and David Waynforth
Algorithms 2024, 17(11), 485; https://doi.org/10.3390/a17110485 - 28 Oct 2024
Viewed by 543
Abstract
Long-term sequelae of coronavirus disease 2019 (COVID-19) infection are common and can have debilitating consequences. There is a need to understand risk factors for Long COVID-19 to give impetus to the development of targeted yet holistic clinical and public health interventions to reduce [...] Read more.
Long-term sequelae of coronavirus disease 2019 (COVID-19) infection are common and can have debilitating consequences. There is a need to understand risk factors for Long COVID-19 to give impetus to the development of targeted yet holistic clinical and public health interventions to reduce its associated healthcare and economic burden. Given the large number and variety of predictors implicated spanning health-related and sociodemographic factors, machine learning becomes a valuable tool. As such, this study aims to employ machine learning to produce an algorithm to predict Long COVID-19 risk, and thereby identify key predisposing factors. Longitudinal cohort data were sourced from the UK’s “Understanding Society: COVID-19 Study” (n = 601 participants with past symptomatic COVID-19 infection confirmed by serology testing). The random forest classification algorithm demonstrated good overall performance with 97.4% sensitivity and modest specificity (65.4%). Significant risk factors included early timing of acute COVID-19 infection in the pandemic, greater number of hours worked per week, older age and financial insecurity. Loneliness and having uncommon health conditions were associated with lower risk. Sensitivity analysis suggested that COVID-19 vaccination is also associated with lower risk, and asthma with an increased risk. The results are discussed with emphasis on evaluating the value of machine learning; potential clinical utility; and some benefits and limitations of machine learning for health science researchers given its availability in commonly used statistical software. Full article
(This article belongs to the Special Issue Advancements in Signal Processing and Machine Learning for Healthcare)
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