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Bioengineering
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Intelligent Health Management, Nursing and Rehabilitation Technology
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Intelligent Health Management, Nursing and Rehabilitation Technology

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 10268

Special Issue Editor

Dr. Ning Deng

E-Mail Website
Guest Editor
College of Biomedical Engineering and Instrumental Science, Zhejiang University, Zhejiang, China
Interests: chronic disease management; artificial intelligent in medicine

Special Issue Information

Dear Colleagues,

The emergence of intelligent health management, nursing and rehabilitation technology signifies a notable fusion of artificial intelligence, machine learning, and modern healthcare practices. This emerging domain aims to enhance traditional rehabilitation processes, refine precise health monitoring, and promote proactive management of chronic conditions. The integration of smart technologies enables real-time monitoring, personalized rehabilitation plans, and reliable clinical decision-making, thus advancing a patient-centric healthcare paradigm.

The scope of this Special Issue encompasses several pivotal aspects vital to the evolution and application of intelligent health management and rehabilitation technology:

  • Proactive Health Systems: Exploring the development and optimization of proactive health systems for real-time health monitoring and management at both individual and community levels.
  • Predictive Models: Investigating the formulation and application of predictive models to identify potential health risks early, optimize rehabilitation plans, and improve the management of chronic conditions.
  • Medical Artificial Intelligence: Delving into the application of AI and ML in supporting clinical decisions, disease diagnosis, and treatment optimization.
  • Traditional Chinese Medicine Health Management: Examining the integration of modern technology with principles of traditional chinese medicine for personalized health and rehabilitation solutions.
  • Smart Healthcare: Encouraging research on designing and implementing smart healthcare solutions leveraging data analytics to enhance patient rehabilitation outcomes and quality of life.
  • Personalized Rehabilitation Strategies: Research focused on utilizing data analytics and machine learning algorithms to design and optimize rehabilitation plans tailored to individual needs for optimal recovery outcomes.
  • Remote Monitoring and Intervention: Investigations into technologies and systems enabling remote health monitoring and real-time interventions, enhancing convenience and effectiveness of rehabilitation processes.
  • Smart Health Devices and Sensors: Exploration of innovative smart devices and sensors for collecting and analyzing health data to support clinical decisions and personalized treatment approaches.

Specifically, the synergy of AI and ML with rehabilitation technology promises to enhance the the development of adaptive rehabilitation protocols. These intelligent systems can evaluate an individual’s progress in real-time, adjusting the rehabilitation plan accordingly to ensure optimal recovery. Furthermore, precise health monitoring, facilitated by smart sensors and wearable devices, delivers invaluable data, guiding informed clinical decisions and timely interventions. Additionally, the proactive management of chronic conditions, bolstered by data analytics, lays the foundation for predictive healthcare, significantly enhancing the quality of life for individuals with chronic ailments.

This Special Issue welcomes original research and insightful reviews exploring the innovative frameworks, methodologies, and applications at the core of intelligent health management and rehabilitation technology. We are particularly interested in contributions that explore the integration of machine learning algorithms with rehabilitation technologies, the development of intelligent health monitoring systems, and the design of personalized rehabilitation strategies that leverage data analytics.

Dr. Ning Deng
Guest Editor

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

  • intelligent rehabilitation technology
  • health monitoring systems
  • machine learning in healthcare
  • intelligent nursing technology
  • data-driven health management

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

Published Papers (5 papers)

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Research

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13 pages, 1623 KiB  
Article
The Comparison of Classical Statistical and Machine Learning Methods in Prediction of Thrombosis in Patients with Acute Myeloid Leukemia
by Ilija Doknić, Mirjana Mitrović, Zoran Bukumirić, Marijana Virijević, Nikola Pantić, Nikica Sabljić, Darko Antić and Živko Bojović
Bioengineering 2025, 12(1), 63; https://doi.org/10.3390/bioengineering12010063 - 13 Jan 2025
Cited by 1 | Viewed by 1016
Abstract
Thrombosis is one of the most frequent complications of cancer, with a potential impact on morbidity and mortality, particularly those with acute myeloid leukemia (AML). Therefore, effective thrombosis prevention is a crucial aspect of cancer management. However, preventive measures against thrombosis may carry [...] Read more.
Thrombosis is one of the most frequent complications of cancer, with a potential impact on morbidity and mortality, particularly those with acute myeloid leukemia (AML). Therefore, effective thrombosis prevention is a crucial aspect of cancer management. However, preventive measures against thrombosis may carry inherent risks and complications. Consequently, the application of thrombosis prevention should be limited to patients with a reasonable risk of developing thrombosis. This thesis explores the potential of data science (DS) methods for predicting venous thrombosis in patients with acute myeloid leukemia. In order to ascertain which patients are at risk, statistical and machine-learning (ML) algorithms were employed to predict which patients with leukemia will develop thrombosis. Multilayer Perceptron (MLP) was found to be the best fit among the models evaluated, achieving the C statistic of 0.749. We examined which attributes are significant and what role they play in prediction and found six significant parameters: sex of the patient, prior history of thrombotic event, type of therapy, international normalized ratio (INR), Eastern Cooperative Oncology Group (ECOG) performance status, and Hematopoietic Cell Transplantation-specific Comorbidity. These findings suggest that subtle DS techniques can improve the prediction of Thrombosis in AML patients, thereby aiding in individual treatment planning. Full article
(This article belongs to the Special Issue Intelligent Health Management, Nursing and Rehabilitation Technology)
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20 pages, 1205 KiB  
Article
Exploring Pain Reduction through Physical Activity: A Case Study of Seven Fibromyalgia Patients
by Marit Dagny Kristine Jenssen, Elisa Salvi, Egil Andreas Fors, Ole Andreas Nilsen, Phuong Dinh Ngo, Miguel Tejedor, Johan Gustav Bellika and Fred Godtliebsen
Bioengineering 2024, 11(8), 765; https://doi.org/10.3390/bioengineering11080765 - 29 Jul 2024
Cited by 2 | Viewed by 1669
Abstract
Fibromyalgia is a chronic disease that affects a considerable fraction of the global population, primarily women. Physical activity is often recommended as a tool to manage the symptoms. In this study, we tried to replicate a positive result of pain reduction through physical [...] Read more.
Fibromyalgia is a chronic disease that affects a considerable fraction of the global population, primarily women. Physical activity is often recommended as a tool to manage the symptoms. In this study, we tried to replicate a positive result of pain reduction through physical activity. After collecting pain and physical activity data from seven women with fibromyalgia, one patient experienced a considerable reduction in pain intensity. According to the patient, the improvement was related to physical activity. Our study was conducted to investigate the replicability of this result through personalized activity recommendations. Out of the other six patients, three experienced a reduction in pain. The remaining three patients did not experience any pain relief. Our results show that two of these were not able to follow the activity recommendations. These results indicate that physical activity may have a positive effect on chronic pain patients. To estimate how effective physical activity can be for this patient group, an intervention with longer follow-ups and larger sample sizes needs to be performed in the future. Full article
(This article belongs to the Special Issue Intelligent Health Management, Nursing and Rehabilitation Technology)
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23 pages, 5152 KiB  
Article
Optimizing Acute Coronary Syndrome Patient Treatment: Leveraging Gated Transformer Models for Precise Risk Prediction and Management
by Yingxue Mei, Zicai Jin, Weiguo Ma, Yingjun Ma, Ning Deng, Zhiyuan Fan and Shujun Wei
Bioengineering 2024, 11(6), 551; https://doi.org/10.3390/bioengineering11060551 - 29 May 2024
Cited by 1 | Viewed by 1126
Abstract
Background: Acute coronary syndrome (ACS) is a severe cardiovascular disease with globally rising incidence and mortality rates. Traditional risk assessment tools are widely used but are limited due to the complexity of the data. Methods: This study introduces a gated Transformer model utilizing [...] Read more.
Background: Acute coronary syndrome (ACS) is a severe cardiovascular disease with globally rising incidence and mortality rates. Traditional risk assessment tools are widely used but are limited due to the complexity of the data. Methods: This study introduces a gated Transformer model utilizing machine learning to analyze electronic health records (EHRs) for an enhanced prediction of major adverse cardiovascular events (MACEs) in ACS patients. The model’s efficacy was evaluated using metrics such as area under the curve (AUC), precision–recall (PR), and F1-scores. Additionally, a patient management platform was developed to facilitate personalized treatment strategies. Results: Incorporating a gating mechanism substantially improved the Transformer model’s performance, especially in identifying true-positive cases. The TabTransformer+Gate model demonstrated an AUC of 0.836, a 14% increase in average precision (AP), and a 6.2% enhancement in accuracy, significantly outperforming other deep learning approaches. The patient management platform enabled healthcare professionals to effectively assess patient risks and tailor treatments, improving patient outcomes and quality of life. Conclusion: The integration of a gating mechanism within the Transformer model markedly increases the accuracy of MACE risk predictions in ACS patients, optimizes personalized treatment, and presents a novel approach for advancing clinical practice and research. Full article
(This article belongs to the Special Issue Intelligent Health Management, Nursing and Rehabilitation Technology)
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17 pages, 1849 KiB  
Article
Unveiling the Unpredictable in Parkinson’s Disease: Sensor-Based Monitoring of Dyskinesias and Freezing of Gait in Daily Life
by Alessandro Zampogna, Luigi Borzì, Domiziana Rinaldi, Carlo Alberto Artusi, Gabriele Imbalzano, Martina Patera, Leonardo Lopiano, Francesco Pontieri, Gabriella Olmo and Antonio Suppa
Bioengineering 2024, 11(5), 440; https://doi.org/10.3390/bioengineering11050440 - 29 Apr 2024
Cited by 7 | Viewed by 2087
Abstract
Background: Dyskinesias and freezing of gait are episodic disorders in Parkinson’s disease, characterized by a fluctuating and unpredictable nature. This cross-sectional study aims to objectively monitor Parkinsonian patients experiencing dyskinesias and/or freezing of gait during activities of daily living and assess possible changes [...] Read more.
Background: Dyskinesias and freezing of gait are episodic disorders in Parkinson’s disease, characterized by a fluctuating and unpredictable nature. This cross-sectional study aims to objectively monitor Parkinsonian patients experiencing dyskinesias and/or freezing of gait during activities of daily living and assess possible changes in spatiotemporal gait parameters. Methods: Seventy-one patients with Parkinson’s disease (40 with dyskinesias and 33 with freezing of gait) were continuously monitored at home for a minimum of 5 days using a single wearable sensor. Dedicated machine-learning algorithms were used to categorize patients based on the occurrence of dyskinesias and freezing of gait. Additionally, specific spatiotemporal gait parameters were compared among patients with and without dyskinesias and/or freezing of gait. Results: The wearable sensor algorithms accurately classified patients with and without dyskinesias as well as those with and without freezing of gait based on the recorded dyskinesias and freezing of gait episodes. Standard spatiotemporal gait parameters did not differ significantly between patients with and without dyskinesias or freezing of gait. Both the time spent with dyskinesias and the number of freezing of gait episodes positively correlated with the disease severity and medication dosage. Conclusions: A single inertial wearable sensor shows promise in monitoring complex, episodic movement patterns, such as dyskinesias and freezing of gait, during daily activities. This approach may help implement targeted therapeutic and preventive strategies for Parkinson’s disease. Full article
(This article belongs to the Special Issue Intelligent Health Management, Nursing and Rehabilitation Technology)
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Review

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30 pages, 4043 KiB  
Review
Advancements and Challenges in Non-Invasive Sensor Technologies for Swallowing Assessment: A Review
by Yuwen Wu, Kai Guo, Yuyi Chu, Zhisen Wang, Hongbo Yang and Juzhong Zhang
Bioengineering 2024, 11(5), 430; https://doi.org/10.3390/bioengineering11050430 - 27 Apr 2024
Cited by 3 | Viewed by 3000
Abstract
Dysphagia is a pervasive health issue that impacts diverse demographic groups worldwide, particularly the elderly, stroke survivors, and those suffering from neurological disorders. This condition poses substantial health risks, including malnutrition, respiratory complications, and increased mortality. Additionally, it exacerbates economic burdens by extending [...] Read more.
Dysphagia is a pervasive health issue that impacts diverse demographic groups worldwide, particularly the elderly, stroke survivors, and those suffering from neurological disorders. This condition poses substantial health risks, including malnutrition, respiratory complications, and increased mortality. Additionally, it exacerbates economic burdens by extending hospital stays and escalating healthcare costs. Given that this disorder is frequently underestimated in vulnerable populations, there is an urgent need for enhanced diagnostic and therapeutic strategies. Traditional diagnostic tools such as the videofluoroscopic swallowing study (VFSS) and flexible endoscopic evaluation of swallowing (FEES) require interpretation by clinical experts and may lead to complications. In contrast, non-invasive sensors offer a more comfortable and convenient approach for assessing swallowing function. This review systematically examines recent advancements in non-invasive swallowing function detection devices, focusing on the validation of the device designs and their implementation in clinical practice. Moreover, this review discusses the swallowing process and the associated biomechanics, providing a theoretical foundation for the technologies discussed. It is hoped that this comprehensive overview will facilitate a paradigm shift in swallowing assessments, steering the development of technologies towards more accessible and accurate diagnostic tools, thereby improving patient care and treatment outcomes. Full article
(This article belongs to the Special Issue Intelligent Health Management, Nursing and Rehabilitation Technology)
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