Design of Reliable Framework for Healthcare Data Assessment

A special issue of Designs (ISSN 2411-9660). This special issue belongs to the section "Bioengineering Design".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 10870

Special Issue Editors


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Co-Guest Editor
M.E., Ph.D, Professor, Department of Electronics and Instrumentation Engg., St. Joseph's College of Engineering, Chennai, India
Interests: mathematical modelling; medical data assesment; machine learning; deep learning; heuristic algorithm based optimization
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Special Issue Information

Dear Colleagues,

In recent years, the occurrence rates of disease in mankind are gradually rising, and efficient screening, decision making and treatment are essential to cure patients with appropriate medication procedures. During both seasonal disease spread as well as the pandemic disease conditions, the number of persons infected with a particular disease will be high, and this situation will initiate a huge diagnostic and treatment burden to hospitals. To handle this situation, most modern hospitals are employing the automatic/semi-automatic approaches for disease screening, data analysis, decision making, drug discovery, and treatment.

The currently accessible software and hardware facilities help to design a considerable number of healthcare monitoring ssystem to support early screening, disease disgnosis, and support for decision making and treatment implementation. Furthermore, the availability of modern communication facilities, such as super computing systems, the Internet of Medical Things (IoMT), and the medical cloud, considerably reduce the disease screening and treatment burdens. Furthermore, the advancements in body area network and remote monitoring facilities are also helping to guide patients to receive the assistance of doctors when there is a need.

The proposed Special Issue aims to receive the research works related to the design and  implementation of different schemes for healthcare data assesment. This Special Issue welcomes innovative research involving various examination techniques linked with clinical reports, bio-signal/bio-image screening, decision making, medication, progress monitoring, data preservation, and sharing though the IoT/Cloud. It also welcomes review articles discussing the current trends in the design of patient-specific healthcare data assesment schemes.

Prof. Dr. Seifedine Kadry
Dr. Venkatesan Rajinikanth
Guest Editors

Manuscript Submission Information

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Keywords

  • design of hardware for healthcare data collection
  • automatic data collection during mass disease screening
  • heuristic algorithm in medical data processing
  • machine-learning-based medical data assessment
  • deep-learning-based medical data assessment
  • machine–¬human interaction for health monitoring
  • smart health assistive devices
  • development of electronic health record (EHR)
  • IoT-associated smart EHR for efficient data exchange
  • remote patient monitoring with IoT and cloud

Published Papers (3 papers)

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Research

22 pages, 7366 KiB  
Article
CLARA: Building a Socially Assistive Robot to Interact with Elderly People
by Adrián Romero-Garcés, Juan Pedro Bandera, Rebeca Marfil, Martín González-García and Antonio Bandera
Designs 2022, 6(6), 125; https://doi.org/10.3390/designs6060125 - 13 Dec 2022
Cited by 2 | Viewed by 2160
Abstract
Although the global population is aging, the proportion of potential caregivers is not keeping pace. It is necessary for society to adapt to this demographic change, and new technologies are a powerful resource for achieving this. New tools and devices can help to [...] Read more.
Although the global population is aging, the proportion of potential caregivers is not keeping pace. It is necessary for society to adapt to this demographic change, and new technologies are a powerful resource for achieving this. New tools and devices can help to ease independent living and alleviate the workload of caregivers. Among them, socially assistive robots (SARs), which assist people with social interactions, are an interesting tool for caregivers thanks to their proactivity, autonomy, interaction capabilities, and adaptability. This article describes the different design and implementation phases of a SAR, the CLARA robot, both from a physical and software point of view, from 2016 to 2022. During this period, the design methodology evolved from traditional approaches based on technical feasibility to user-centered co-creative processes. The cognitive architecture of the robot, CORTEX, keeps its core idea of using an inner representation of the world to enable inter-procedural dialogue between perceptual, reactive, and deliberative modules. However, CORTEX also evolved by incorporating components that use non-functional properties to maximize efficiency through adaptability. The robot has been employed in several projects for different uses in hospitals and retirement homes. This paper describes the main outcomes of the functional and user experience evaluations of these experiments. Full article
(This article belongs to the Special Issue Design of Reliable Framework for Healthcare Data Assessment)
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12 pages, 3905 KiB  
Article
Enhanced Heart Disease Prediction Based on Machine Learning and χ2 Statistical Optimal Feature Selection Model
by Raniya R. Sarra, Ahmed M. Dinar, Mazin Abed Mohammed and Karrar Hameed Abdulkareem
Designs 2022, 6(5), 87; https://doi.org/10.3390/designs6050087 - 29 Sep 2022
Cited by 33 | Viewed by 5426
Abstract
Automatic heart disease prediction is a major global health concern. Effective cardiac treatment requires an accurate heart disease prognosis. Therefore, this paper proposes a new heart disease classification model based on the support vector machine (SVM) algorithm for improved heart disease detection. To [...] Read more.
Automatic heart disease prediction is a major global health concern. Effective cardiac treatment requires an accurate heart disease prognosis. Therefore, this paper proposes a new heart disease classification model based on the support vector machine (SVM) algorithm for improved heart disease detection. To increase prediction accuracy, the χ2 statistical optimum feature selection technique was used. The suggested model’s performance was then validated by comparing it to traditional models using several performance measures. The proposed model increased accuracy from 85.29% to 89.7%. Additionally, the componential load was reduced by half. This result indicates that our system outperformed other state-of-the-art methods in predicting heart disease. Full article
(This article belongs to the Special Issue Design of Reliable Framework for Healthcare Data Assessment)
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16 pages, 9616 KiB  
Article
Design of Virtual Reality-Based Hippotherapy Simulator Exergaming Software and Its Controller for Rehabilitation of Children with Cerebral Palsy in Indonesia: An Engineering Concept
by Ardianto Satriawan, Wildan Trusaji, Muhammad Ogin Hasanuddin, Septia Susanti Rahadini, Mayang Cendikia Selekta and Ellyana Sungkar
Designs 2022, 6(5), 76; https://doi.org/10.3390/designs6050076 - 1 Sep 2022
Cited by 2 | Viewed by 2383
Abstract
Horse riding exercise, also known as hippotherapy is a popular treatment for children with cerebral palsy (CP). However, the need for trained therapist, massive land use, and expensive maintenance of the horse ranch makes hippotherapy not affordable or even available for most patients [...] Read more.
Horse riding exercise, also known as hippotherapy is a popular treatment for children with cerebral palsy (CP). However, the need for trained therapist, massive land use, and expensive maintenance of the horse ranch makes hippotherapy not affordable or even available for most patients in Indonesia. This problem motivates us to consider mechanical horse riding simulator machines to replace actual horse hippotherapy. However, most patients are children and are easily bored when asked to do monotonous activities for an extended period. The room setting also does not give the patient visual inputs that usually help motivates the children in real-horse hippotherapy activities. To solve this problem, we designed an exercise game (exergaming) software which we named Sirkus Apel, providing the patients with fun activities while doing the therapy. We also design an inertial sensor-based controller that lets the patients control the in-game horse by their back movements, which also benefits CP patients. To make the visual input enjoyable to the user while also considering the user’s safety, we built a convex mirror-based dome virtual reality to provide an immersive 3-D experience. We then project the game content to the dome to provide an immersive experience to the patients making it as if they are riding a real horse inside the game. Full article
(This article belongs to the Special Issue Design of Reliable Framework for Healthcare Data Assessment)
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