Semantic Interoperability for Development of Future Health Care: A Systematic Review of Different Technologies

Patange, Gajanan; Sonara, Zankhan; Bhatt, Harmish

doi:10.1007/978-981-33-4355-9_42

Cited by 2 publications

(1 citation statement)

References 17 publications

(18 reference statements)

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“…Semantic Interoperability: This important phase deals with how everyone involved understands and interprets the data (de Mello et al, 2022;Jaulent et al, 2018;Patange et al, 2021). As of now, the main focus is on ensuring semantic meaning and structural consistency of data transferred between systems.…”

Section: Healthcare Systems Interoperabilitymentioning

confidence: 99%

A review of distributed ledger technologies application in medical systems interoperability

Bundi,

Mutua,

Karume

2024

AJSTSS

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This study of the literature delves into the complex area of medical systems interoperability, focusing on mitigating variables that impact security and data transfer at the structural and semantic levels. In the era of digital healthcare, the secure sharing of medical data is crucial, and this study looks at how Distributed Ledger Technologies (DLTs) can play a major role in addressing these challenges. Complex interoperability issues that come from differences in communication protocols, data formats, established data structures, data models, and data meaning and codification methodologies face the healthcare industry. These problems typically impede the seamless transmission of electronic medical records between healthcare systems. Because of their decentralized structure and cryptographic foundation, DLTs offer a workable solution to these issues. By critically evaluating previous research and case studies, DLTs may be able to lessen these interoperability issues, as this literature review illustrates. Since DLTs provide an immutable and secure platform for the transmission of medical data, guaranteeing data integrity and confidentiality, they are a natural fit for the sensitive nature of healthcare data. Their importance in creating safe communication protocols, enhancing the meaning of data, and defining models and formats for data is emphasized in this review. A comprehensive architecture for DLT interoperability in healthcare is also recommended by the research. This framework encourages the development of DLT integration, shared data models, standardized data formats, and governance and policy. By implementing this strategy and strengthening secure medical data sharing, healthcare organizations and governments may increase the efficiency, precision, and speed of healthcare delivery. The crucial role that DLTs play in removing the structural and semantic barriers to safe medical systems interoperability is highlighted in the conclusion of this literature review. By adopting DLTs, the healthcare sector may usher in a new era of standardized, safe, and efficient medical data transmission, which will ultimately benefit both patients and healthcare providers. This study shows how distributed ledger technologies (DLTs) have the potential to revolutionize the healthcare industry by enabling the secure and meaningful exchange of medical data between different systems, thereby improving patient care and healthcare outcomes.

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Section: Healthcare Systems Interoperabilitymentioning

confidence: 99%

A review of distributed ledger technologies application in medical systems interoperability

Bundi,

Mutua,

Karume

2024

AJSTSS

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Improved Multimedia Object Processing for the Internet of Vehicles

Bhatia

Alsuwailam

Roy

et al. 2022

Sensors

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The combination of edge computing and deep learning helps make intelligent edge devices that can make several conditional decisions using comparatively secured and fast machine learning algorithms. An automated car that acts as the data-source node of an intelligent Internet of vehicles or IoV system is one of these examples. Our motivation is to obtain more accurate and rapid object detection using the intelligent cameras of a smart car. The competent supervision camera of the smart automobile model utilizes multimedia data for real-time automation in real-time threat detection. The corresponding comprehensive network combines cooperative multimedia data processing, Internet of Things (IoT) fact handling, validation, computation, precise detection, and decision making. These actions confront real-time delays during data offloading to the cloud and synchronizing with the other nodes. The proposed model follows a cooperative machine learning technique, distributes the computational load by slicing real-time object data among analogous intelligent Internet of Things nodes, and parallel vision processing between connective edge clusters. As a result, the system increases the computational rate and improves accuracy through responsible resource utilization and active–passive learning. We achieved low latency and higher accuracy for object identification through real-time multimedia data objectification.

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Semantic Interoperability for Development of Future Health Care: A Systematic Review of Different Technologies

Cited by 2 publications

References 17 publications

A review of distributed ledger technologies application in medical systems interoperability

A review of distributed ledger technologies application in medical systems interoperability

Improved Multimedia Object Processing for the Internet of Vehicles

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