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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 December 2023) | Viewed by 38912

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Special Issue Editors

Prof. Dr. Chien-Hung Yeh

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Guest Editor

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: electrophysiological signal analysis; intelligent medical treatment; disease health and safety prevention and control; brain science
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xiaojuan Ban

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Guest Editor

School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: artificial intelligence; human–brain interface; 3D visualization technique
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Men-Tzung Lo

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Guest Editor

Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 32001, Taiwan
Interests: bioelectronic devices; signal processing; smart health care; cardiac electrophysiology
Special Issues, Collections and Topics in MDPI journals

Dr. Wenbin Shi

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Guest Editor

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: signal processing; brain science; smart health care; artificial intelligence and its application
Special Issues, Collections and Topics in MDPI journals

Dr. Shenghong He

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Guest Editor

1. MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, UK
2. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
Interests: movement disorders; deep brain stimulation; brain–computer interface; tremor; motor control

Special Issue Information

Dear Colleagues,

Intelligent medicine and health care are flourishing due to the integration of interdisciplinary factors, including artificial intelligence, electrophysiology, signal processing, medical image, complex theory, electronics, and clinics. We are entering an era of in which multidimensional and big data play large roles in developing applications for medicine and health care.

There are many state-of-the-art technologies and new application developments dealing with advanced data analysis and learning, embedded artificial intelligence, clinical decision support, patient ubiquitous monitoring, and rehabilitation aspects.

The Special Issue aims to collect recent research on emerging interdisciplinary methods/techniques/systems for intelligent medicine and health care. Potential topics include, but are not limited, to the following:

E-healthcare;
Artificial intelligence and machine learning for medicine and health care;
Electrophysiological or image processing methods for medicine and health care;
Nonlinear dynamics and chaos in health and diseases;
Human-centric computer interfaces for health-related environments;
Security and privacy models for medical and healthcare systems;
Interoperability of heterogeneous network and software technologies for medical and healthcare systems;
Smart sensors and wearable devices for medical and healthcare systems;
Health data analytics and personalized models in medical and healthcare environments;
Neuromodulation and decoding for medical and healthcare systems.

Prof. Dr. Chien-Hung Yeh
Prof. Dr. Xiaojuan Ban
Prof. Dr. Men-Tzung Lo
Dr. Wenbin Shi
Dr. Shenghong He
Guest Editors

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Intelligent Medicine and Health Care, 2nd Edition in Applied Sciences (2 articles)

Published Papers (10 papers)

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Research

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13 pages, 1021 KiB

Open AccessArticle

Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach

by Nirmal Acharya, Padmaja Kar, Mustafa Ally and Jeffrey Soar

Appl. Sci. 2024, 14(4), 1630; https://doi.org/10.3390/app14041630 - 18 Feb 2024

Viewed by 1679

Abstract

Significant clinical overlap exists between mental health and substance use disorders, especially among women. The purpose of this research is to leverage an AutoML (Automated Machine Learning) interface to predict and distinguish co-occurring mental health (MH) and substance use disorders (SUD) among women. By employing various modeling algorithms for binary classification, including Random Forest, Gradient Boosted Trees, XGBoost, Extra Trees, SGD, Deep Neural Network, Single-Layer Perceptron, K Nearest Neighbors (grid), and a super learning model (constructed by combining the predictions of a Random Forest model and an XGBoost model), the research aims to provide healthcare practitioners with a powerful tool for earlier identification, intervention, and personalised support for women at risk. The present research presents a machine learning (ML) methodology for more accurately predicting the co-occurrence of mental health (MH) and substance use disorders (SUD) in women, utilising the Treatment Episode Data Set Admissions (TEDS-A) from the year 2020 (n = 497,175). A super learning model was constructed by combining the predictions of a Random Forest model and an XGBoost model. The model demonstrated promising predictive performance in predicting co-occurring MH and SUD in women with an AUC = 0.817, Accuracy = 0.751, Precision = 0.743, Recall = 0.926 and F1 Score = 0.825. The use of accurate prediction models can substantially facilitate the prompt identification and implementation of intervention strategies. Full article

(This article belongs to the Special Issue Intelligent Medicine and Health Care)

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19 pages, 3161 KiB

Open AccessArticle

A Novel Criticality Analysis Method for Assessing Obesity Treatment Efficacy

by Shadi Eltanani, Tjeerd V. olde Scheper, Mireya Muñoz-Balbontin, Arantza Aldea, Jo Cossington, Sophie Lawrie, Salvador Villalpando-Carrion, Maria Jose Adame, Daniela Felgueres, Clare Martin and Helen Dawes

Appl. Sci. 2023, 13(24), 13225; https://doi.org/10.3390/app132413225 - 13 Dec 2023

Cited by 1 | Viewed by 1214

Abstract

Human gait is a significant indicator of overall health and well-being due to its dependence on metabolic requirements. Abnormalities in gait can indicate the presence of metabolic dysfunction, such as diabetes or obesity. However, detecting these can be challenging using classical methods, which often involve subjective clinical assessments or invasive procedures. In this work, a novel methodology known as Criticality Analysis (CA) was applied to the monitoring of the gait of teenagers with varying amounts of metabolic stress who are taking part in an clinical intervention to increase their activity and reduce overall weight. The CA approach analysed gait using inertial measurement units (IMU) by mapping the dynamic gait pattern into a nonlinear representation space. The resulting dynamic paths were then classified using a Support Vector Machine (SVM) algorithm, which is well-suited for this task due to its ability to handle nonlinear and dynamic data. The combination of the CA approach and the SVM algorithm demonstrated high accuracy and non-invasive detection of metabolic stress. It resulted in an average accuracy within the range of 78.2% to 90%. Additionally, at the group level, it was observed to improve fitness and health during the period of the intervention. Therefore, this methodology showed a great potential to be a valuable tool for healthcare professionals in detecting and monitoring metabolic stress, as well as other associated disorders. Full article

(This article belongs to the Special Issue Intelligent Medicine and Health Care)

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18 pages, 2280 KiB

Open AccessArticle

A Modified and Effective Blockchain Model for E-Healthcare Systems

by Basem Assiri

Appl. Sci. 2023, 13(23), 12630; https://doi.org/10.3390/app132312630 - 23 Nov 2023

Cited by 6 | Viewed by 1259

Abstract

The development of e-healthcare systems requires the application of advanced technologies, such as blockchain technology. The main challenge of applying blockchain technology to e-healthcare is to handle the impact of the delay that results from blockchain procedures during the communication and voting phases. The impacts of latency in blockchains negatively influence systems’ efficiency, performance, real-time processing, and quality of service. Therefore, this work proposes a modified model of a blockchain that allows delays to be avoided in critical situations in healthcare. Firstly, this work analyzes the specifications of healthcare data and processes to study and classify healthcare transactions according to their nature and sensitivity. Secondly, it introduces the concept of a fair-proof-of-stake consensus protocol for block creation and correctness procedures rather than famous ones such as proof-of-work or proof-of-stake. Thirdly, the work presents a simplified procedure for block verification, where it classifies transactions into three categories according to the time period limit and trustworthiness level. Consequently, there are three kinds of blocks, since every category is stored in a specific kind of block. The ideas of time period limits and trustworthiness fit with critical healthcare situations and the authority levels in healthcare systems. Therefore, we reduce the validation process of the trusted blocks and transactions. All proposed modifications help to reduce computational costs, speed up processing times, and enhance security and privacy. The experimental results show that the total execution time using a modified blockchain is reduced by about 49% compared to traditional blockchain models. Additionally, the number of messages using modified blockchain is reduced by about 53% compared to the traditional blockchain model. Full article

(This article belongs to the Special Issue Intelligent Medicine and Health Care)

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12 pages, 8107 KiB

Open AccessArticle

The Centralization and Sharing of Information for Improving a Resilient Approach Based on Decision-Making at a Local Home Health Care Center

by Guillaume Dessevre, Cléa Martinez, Liwen Zhang, Christophe Bortolaso and Franck Fontanili

Appl. Sci. 2023, 13(15), 8576; https://doi.org/10.3390/app13158576 - 25 Jul 2023

Cited by 1 | Viewed by 1105

Abstract

Home care centers face both an increase in demand and many variations during the execution of routes, compromising the routes initially planned; robust solutions are not effective enough, and it is necessary to move on to resilient approaches. We create a close-to-reality use case supported by interviews of staff at home health care centers, where caregivers are faced with unexpected events that compromise their initial route. We model, analyze, and compare two resilient approaches to deal with these disruptions: a distributed collaborative approach and a centralized collaborative approach, where we propose a centralization and sharing of information to improve local decision-making. The latter reduces the number of late arrivals by 11%, the total time of late arrival by 21%, and halves the number of routes exceeding the end of work time (contrary to the distributed collaborative approach due to the time wasted reaching colleagues). The use of a device, such as a smartphone application, to centralize and share information thus, allows better mutual assistance between caregivers. Moreover, we highlight several possible openings, like the coupling of simulation and optimization, to propose a more resilient approach. Full article

(This article belongs to the Special Issue Intelligent Medicine and Health Care)

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29 pages, 2134 KiB

Open AccessArticle

Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones

by Rytis Maskeliūnas, Robertas Damaševičius, Tomas Blažauskas, Kipras Pribuišis, Nora Ulozaitė-Stanienė and Virgilijus Uloza

Appl. Sci. 2023, 13(9), 5363; https://doi.org/10.3390/app13095363 - 25 Apr 2023

Cited by 3 | Viewed by 1976

Abstract

Multiparametric indices offer a more comprehensive approach to voice quality assessment by taking into account multiple acoustic parameters. Artificial intelligence technology can be utilized in healthcare to evaluate data and optimize decision-making processes. Mobile devices provide new opportunities for remote speech monitoring, allowing the use of basic mobile devices as screening tools for the early identification and treatment of voice disorders. However, it is necessary to demonstrate equivalence between mobile device signals and gold standard microphone preamplifiers. Despite the increased use and availability of technology, there is still a lack of understanding of the impact of physiological, speech/language, and cultural factors on voice assessment. Challenges to research include accounting for organic speech-related covariables, such as differences in conversing voice sound pressure level (SPL) and fundamental frequency (f0), recognizing the link between sensory and experimental acoustic outcomes, and obtaining a large dataset to understand regular variation between and within voice-disordered individuals. Our study investigated the use of cellphones to estimate the Acoustic Voice Quality Index (AVQI) in a typical clinical setting using a Pareto-optimized approach in the signal processing path. We found that there was a strong correlation between AVQI results obtained from different smartphones and a studio microphone, with no significant differences in mean AVQI scores between different smartphones. The diagnostic accuracy of different smartphones was comparable to that of a professional microphone, with optimal AVQI cut-off values that can effectively distinguish between normal and pathological voice for each smartphone used in the study. All devices met the proposed 0.8 AUC threshold and demonstrated an acceptable Youden index value. Full article

(This article belongs to the Special Issue Intelligent Medicine and Health Care)

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16 pages, 1913 KiB

Open AccessArticle

CNN-Based Pill Image Recognition for Retrieval Systems

by Khalil Al-Hussaeni, Ioannis Karamitsos, Ezekiel Adewumi and Rema M. Amawi

Appl. Sci. 2023, 13(8), 5050; https://doi.org/10.3390/app13085050 - 18 Apr 2023

Cited by 5 | Viewed by 5360

Abstract

Medication should be consumed as prescribed with little to zero margins for errors, otherwise consequences could be fatal. Due to the pervasiveness of camera-equipped mobile devices, patients and practitioners can easily take photos of unidentified pills to avert erroneous prescriptions or consumption. This area of research goes under the umbrella of information retrieval and, more specifically, image retrieval or recognition. Several studies have been conducted in the area of image retrieval in order to propose accurate models, i.e., accurately matching an input image with stored ones. Recently, neural networks have been shown to be effective in identifying digital images. This study aims to provide an enhancement to image retrieval in terms of accuracy and efficiency through image segmentation and classification. This paper suggests three neural network (CNN) architectures: two models that are hybrid networks paired with a classification method (CNN+SVM and CNN+kNN) and one ResNet-50 network. We perform various preprocessing steps by using several detection techniques on the selected dataset. We conduct extensive experiments using a real-life dataset obtained from the National Library of Medicine database. The results demonstrate that our proposed model is capable of deriving an accuracy of 90.8%. We also provide a comparison of the above-mentioned three models with some existing methods, and we notice that our proposed CNN+kNN architecture improved the pill image retrieval accuracy by 10% compared to existing models. Full article

(This article belongs to the Special Issue Intelligent Medicine and Health Care)

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15 pages, 5725 KiB

Open AccessArticle

Cross-Platform Gait Analysis and Fall Detection Wearable Device

by Ming-Hung Chang, Yi-Chao Wu, Hsi-Yu Niu, Yi-Ting Chen and Shu-Han Juang

Appl. Sci. 2023, 13(5), 3299; https://doi.org/10.3390/app13053299 - 4 Mar 2023

Cited by 1 | Viewed by 2174

Abstract

Since the fall was often occurred in elders daily, this paper focused on gait analysis with fall detection to develop a wearable device. To ensure that the mobile application, APP, could be used in different platform of mobile phone, such Android or iOS, the designed wearable device also could be used in cross-platform in mobile phone. Therefore, a cross-platform gait analysis and fall detection wearable device (CPGAFDWD) was proposed. Since CPGAFDWD APP was used in web browser without limiting to platform, it could be used for different platforms of mobile phone. The gait analysis could be detected at home. The fall detection also could be executed in any place immediately. The patients and medical staff all could query the status of rehabilitation in any place and any time via the Internet. The experimental results showed that the correct rate of gait analysis and fall detection could be up to 90% in cross-platform of mobile phone. In the future, CPGAFDWD will be planned to be verified by Institutional Review Board, IRB, for clinical treatment. Full article

(This article belongs to the Special Issue Intelligent Medicine and Health Care)

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20 pages, 9336 KiB

Open AccessArticle

Trust Components: An Analysis in The Development of Type 2 Diabetic Mellitus Mobile Application

by Salaki Reynaldo Joshua, Wasim Abbas, Je-Hoon Lee and Seong Kun Kim

Appl. Sci. 2023, 13(3), 1251; https://doi.org/10.3390/app13031251 - 17 Jan 2023

Cited by 5 | Viewed by 3357

Abstract

Trust in information and communication technology devices is an important factor, considering the role of technology in carrying out supporting tasks in everyday human activities. The level of trust in technology will influence its application and adoption. Recognizing the importance of trust in technology, researchers in this study will examine trust components for the development of a type 2 diabetes mobile application. The results of this study resulted in three major focuses, namely the application design (consisting of architecture), UI design, and evaluation of trust factors of the application: functionality, ease of use, usefulness, security and privacy, and cost. This analysis of trust components will be useful for the application or adoption by users of a type 2 diabetes mellitus mobile application so that users will trust the application both in terms of functionality and the generated information. Full article

(This article belongs to the Special Issue Intelligent Medicine and Health Care)

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16 pages, 4085 KiB

Open AccessArticle

M-Healthcare Model: An Architecture for a Type 2 Diabetes Mellitus Mobile Application

by Salaki Reynaldo Joshua, Wasim Abbas and Je-Hoon Lee

Appl. Sci. 2023, 13(1), 8; https://doi.org/10.3390/app13010008 - 20 Dec 2022

Cited by 6 | Viewed by 3696

Abstract

Type 2 diabetes mellitus (T2DM) is a metabolic disorder wherein the patients require DM management to keep their blood glucose under proper and regular control. Diabetes mellitus can be managed with the help of technologies, one of which is mobile health. Mobile health is an innovation in telemedicine that utilizes gadgets as a medium to access digitally based health information and services by utilizing electronic devices connected to the Internet. Mobile health services are distinguished based on interactions between users and medical personnel; namely, interactive and non-interactive services. The developed application can integrate Android mobile application software with supporting hardware, such as a glucometer, a wearable band, a heart rate sensor, a treadmill, and an exercise bike. The provided features in this mobile application include the monitoring of medication, food intake, exercise, and sleep. This study’s goal was to create a mobile application architecture for type 2 diabetes mellitus mobile applications. This research focused on developing an architecture for mobile diabetes applications, a hardware block diagram design, and an architecture of sensors for a type 2 diabetes mellitus mobile application. Full article

(This article belongs to the Special Issue Intelligent Medicine and Health Care)

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Review

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11 pages, 577 KiB

Open AccessReview

Biomechanical, Healing and Therapeutic Effects of Stretching: A Comprehensive Review

by Elissaveta Zvetkova, Eugeni Koytchev, Ivan Ivanov, Sergey Ranchev and Antonio Antonov

Appl. Sci. 2023, 13(15), 8596; https://doi.org/10.3390/app13158596 - 26 Jul 2023

Cited by 6 | Viewed by 15376

Abstract

Characterized in biomedical terms, stretching exercises have been defined as movements applied by external and/or internal forces to increase muscle and joint flexibility, decrease muscle stiffness, elevate the joint range of motion (ROM), increase the length of the “muscle–tendon” morpho-functional unit, and improve joint, muscle, and tendon movements, contraction, and relaxation. The present review examines and summarizes the initial and recent literature data related to the biomechanical, physiological, and therapeutic effects of static stretching (SS) on flexibility and other physiological characteristics of the main structure and the “joint–ligament–tendon–muscle” functional unit. The healing and therapeutic effects of SS, combined with other rehabilitation techniques (massage, foam rolling with and without vibrations, hot/cold therapy, etc.), are discussed in relation to the creation of individual (patient-specific) or group programs for the treatment and prevention of joint injuries, as well as for the improvement of performance in sports. From a theoretical point of view, the role of SS in positively affecting the composition of the connective tissue matrix is pointed out: types I–III collagen syntheses, hyaluronic acid, and glycosaminoglycan (GAG) turnover under the influence of the transforming growth factor beta-1 (TGF-β-1). Different variables, such as collagen type, biochemistry, elongation, and elasticity, are used as molecular biomarkers. Recent studies have indicated that static progressive stretching therapy can prevent/reduce the development of arthrogenic contractures, joint capsule fibrosis, and muscle stiffness and requires new clinical applications. Combined stretching techniques have been proposed and applied in medicine and sports, depending on their long- and short-term effects on variables, such as the ROM, EMG activity, and muscle stiffness. The results obtained are of theoretical and practical interest for the development of new experimental, mathematical, and computational models and the creation of efficient therapeutic programs. The healing effects of SS on the main structural and functional unit—“joint–ligament–tendon–muscle”—need further investigation, which can clarify and evaluate the benefits of SS in prophylaxis and the treatment of joint injuries in healthy and ill individuals and in older adults, compared to young, active, and well-trained persons, as well as compared to professional athletes. Full article

(This article belongs to the Special Issue Intelligent Medicine and Health Care)

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