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Applied Sciences
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New Perspective on Machine Learning in Healthcare Informatics
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New Perspective on Machine Learning in Healthcare Informatics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 571

Special Issue Editors

Dr. Xiaojin Li

E-Mail Website
Guest Editor
Department of Neurology, McGovern Medical School, 7000 Fannin St, Houston, TX 77030, USA
Interests: big data analytics using machine learning and deep learning for healthcare applications, such as epileptic seizure detection and prediction, sleep stage scoring, sleep spindle detection, Post-ictal Generalized EEG Suppression (PGES) detection, etc.
Dr. Licong Cui

E-Mail Website
Guest Editor
McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Houston, TX 77030, USA
Interests: biomedical informatics

Special Issue Information

Dear Colleagues,

This Special Issue offers an insightful exploration into the transformative potential of machine learning (ML) within the realm of healthcare informatics. ML techniques increasingly permeate various facets of the healthcare industry, ranging from diagnostics and treatment optimization to patient care management. This collection provides a comprehensive overview of innovative methodologies, applications, and case studies. By showcasing the versatility and efficacy of ML algorithms, healthcare professionals can harness vast amounts of data to make informed decisions and deliver personalized patient care. Furthermore, this Special Issue delves into critical considerations such as data privacy, model interpretability, ethical implications, and the importance of evaluation strategies that align with real clinical practice. Through these discussions, the responsible and effective integration of ML in healthcare settings is elucidated, promising advancements that enhance both patient outcomes and healthcare delivery.

Dr. Xiaojin Li
Dr. Licong Cui
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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 and deep learning
  • computational healthcare
  • big data analytics
  • clinical decision support
  • precision health
  • intelligent healthcare systems
  • data privacy
  • interoperability

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Published Papers (1 paper)

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Research

24 pages, 4562 KiB  
Article
The Potential of Artificial Intelligence in Predicting Post-Stroke Rehabilitation Outcomes: Statistical Analysis Considering Rivermead Motor Assessment and Activities of Daily Living Indicators and Selected Demographic Variables
by Małgorzata Kuźnar and Augustyn Lorenc
Appl. Sci. 2024, 14(24), 11806; https://doi.org/10.3390/app142411806 - 17 Dec 2024
Viewed by 285
Abstract
Strokes are currently the third most common cause of death worldwide and the leading cause of disability in people over 50 years of age. The functioning of post-stroke patients depends primarily on well-conducted rehabilitation, both in stationary conditions and at home. The aim [...] Read more.
Strokes are currently the third most common cause of death worldwide and the leading cause of disability in people over 50 years of age. The functioning of post-stroke patients depends primarily on well-conducted rehabilitation, both in stationary conditions and at home. The aim of this study was to evaluate the functional outcomes of patients after ischemic stroke who underwent home rehabilitation. The RMA (Rivermead Motor Assessment) and ADL (activities of daily living) scales were used for evaluation. A total of 20 patients underwent a 4-week home rehabilitation program in Cracow. In the studied group, most patients showed functional improvement after the 4-week rehabilitation period. Predictive models were created (Net1, Net2, Net3) using artificial intelligence algorithms, including regression and classification methods. The analysis results indicate that the best outcomes in predicting the RMA and ADL indicators. For Net2, the prediction accuracy for the ADL indicator was 94.4%, which is significantly higher compared to the other indicators. The RMA1-3 indicators achieved relatively low accuracy rates of 38.9–44.4%. In contrast, for Net3, the RMA1-3 indicators showed high accuracy, achieving 89.1–91.3% correct results. The conclusions of the study suggest that using a combination of the Net2 and Net3 models can contribute to optimizing the rehabilitation process, allowing therapy to be tailored to the individual needs of patients. The research proves that it is possible to predict the effect of rehabilitation by using AI. The implementation of such solutions can increase the effectiveness of post-stroke rehabilitation, particularly through the personalization of therapy and dynamic monitoring of patient progress. Full article
(This article belongs to the Special Issue New Perspective on Machine Learning in Healthcare Informatics)
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