Incorporating Artificial Intelligence into Medical Cyber Physical Systems: A Survey

Shishvan, Omid Rajabi; Zois, Daphney–Stavroula; Soyata, Tolga

doi:10.1007/978-3-030-27844-1_8

Cited by 11 publications

(7 citation statements)

References 60 publications

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“…The high availability of patient-centric and secure data by the use of technologies such as Blockchain technologies (Dubovitskaya et al, 2020) has resulted in healthcare service delivery being personalised and unique as per the needs of the patients. The large amount of big data which are gathered through IoT sensors in an MCPS provides a rich platform, which artificial intelligence algorithms can use to provide decision support for both medical professionals and patients to create a unique healthcare service model (Shishvan et al, 2020). Digital twins are the digital replica of the physical object.…”

Section: Mcps Impact On Quality and Person-centredness Dimensions Of ...mentioning

confidence: 99%

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The impact of medical cyber–physical systems on healthcare service delivery

Sony

Antony

McDermott

2022

TQM

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PurposeThe pandemic has reinforced the need for revamping the healthcare service delivery systems around the world to meet the increased challenges of modern-day illnesses. The use of medical cyber–physical system (MCPS) in the healthcare is one of the means of transforming the landscape of the traditional healthcare service delivery system. The purpose of this study is to critically examine the impact of MCPS on the quality of healthcare service delivery.Design/methodology/approachThis paper uses an evidence-based approach, the authors have conducted a systematic literature review to study the impact of MCPS on healthcare service delivery. Fifty-four articles were thematically examined to study the impact of MCPS on eight characteristics of the healthcare service delivery proposed by the world health organisation.FindingsThe study proposes support that MCPS will positively impact (1) comprehensiveness, (2) accessibility, (3) coverage, (4) continuity, (5) quality, (6) person-centredness, (7) coordination, (8) accountability and (9) efficiency dimension of the healthcare service delivery. The study further draws nine propositions to support the impact of MCPS on the healthcare service delivery.Practical implicationsThis study can be used by stakeholders as a guide point while using MCPS in healthcare service delivery systems. Besides, healthcare managers can use this study to understand the performance of their healthcare system. This study can further be used for designing effective strategies for deploying MCPS to be effective and efficient in each of the dimensions of healthcare service delivery.Originality/valueThe previous studies have focussed on technology aspects of MCPS and none of them critically analysed the impact on healthcare service delivery. This is the first literature review carried out to understand the impact of MCPS on the nine dimensions of healthcare service delivery proposed by WHO. This study provides improved thematic awareness of the resulting body of knowledge, allowing the field of MCPS and healthcare service delivery to progress in a more informed and multidisciplinary manner.

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Section: Mcps Impact On Quality and Person-centredness Dimensions Of ...mentioning

confidence: 99%

“…, 2018) and they are used in hospitals for improving continuous high-quality healthcare. In other words, MCPS are a “ platform in which the patient health parameters are acquired by the use of emergent technologies and processed via advanced machine intelligence algorithms in the cloud” (Shishvan et al. , 2020).…”

Section: Introductionmentioning

confidence: 99%

The impact of medical cyber–physical systems on healthcare service delivery

Sony

Antony

McDermott

2022

TQM

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“…The authors of a recent survey [7] highlight the importance of incorporating intelligence into MCPS. The study reveals that the emerging health applications increasing need to include machine intelligence to provide innovative and smart services.…”

Section: Related Workmentioning

confidence: 99%

“…The interconnection between cyber and physical world are usually assisted by a feedback-loop control system [4] [3] [5], which enables CPS to be more adaptive to the changes in the physical world as sensed by various IoT sensing devices. The processing of diverse and dynamic data sources using different machine learning (ML) algorithms enable the CPS to exhibit higher intelligence [6] [7], which supports various application domains, including smart healthcare [8], transportation and others.…”

Section: Introductionmentioning

confidence: 99%

“…coronary disease, diabetes, cancer, etc.). Processing of body-worn sensor data in a meaningful way using ML techniques has brought enormous complexity due to the diversity of health data [13], prompting new research in this domain [14] [7], [15]. The NCDs are not transferable and non-contagious [16], although more life-threatening than contagious diseases.…”

Section: Introductionmentioning

confidence: 99%

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Early-Stage Risk Prediction of Non-Communicable Disease Using Machine Learning in Health CPS

2021

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Cyber-Physical Systems (CPS) embed computation and communication capability into its core to regulate physical processes and seamlessly mediate between the cyber and the physical world for various control and monitoring tasks. Health CPS, a variant of CPS in the healthcare sector, acts as a health monitoring system to dynamically capture, process, and analyze health sensor data through integrated internet of things (IoT)-enabled cyber-physical processes. These systems can suitably support patients suffering from non-communicable diseases (NCDs) or who are at risk of suffering from those. Identifying the risk of NCDs, such as heart disease and diabetes, requires artificial intelligence (AI) techniques into the core of health CPS. Recently, there has been growing interest to incorporate machine learning into CPS, which can facilitate the disease classification, detection, monitoring, and prediction of several NCDs. However, there is a shortage of visible work that focus on early-stage risk prediction of these diseases. In this work, we propose a novel machine learning based health CPS framework that addresses the challenge of effectively processing the wearable IoT sensor data for early risk prediction of diabetes as an example of NCDs. In the experiment, a verified diabetic dataset has been used for training, while the testing has been performed on an artificially generated data collection from sensors. The experiment with several machine learning algorithms shows the effectiveness of the proposed approach in achieving the maximum precision from the Random Tree algorithm, which requires a minimum time of 0.01s to construct a model and obtains 94% accuracy to predict the probability of diabetes at an early point.

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