CEECP: CT-based enhanced e-clinical pathways in terms of processing time to enable big data analytics in healthcare along with cloud computing

Velpula, Prasad; Pamula, Rajendra

doi:10.1016/j.cie.2022.108037

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…Data science techniques such as dimensionality reduction, rule-based mining, natural language processing, machine learning, and deep learning have been employed in many healthcare areas to enhance health care applications. [38][39][40][41] Computational enhancement applications, such as machine learning, deep learning, and artificial intelligence, have been deployed in many sectors, where their input has afforded quality service delivery. In the healthcare arena, related work such as "natural language processing", "machine learning", and "deep learning" has been done.…”

Section: A Data-driven Paradigm For Resilient and Sustainable His For...mentioning

confidence: 99%

A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications

Epizitone,

Moyane,

Agbehadji

2023

JMDH

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Many transformations and uncertainties, such as the fourth industrial revolution and pandemics, have propelled healthcare acceptance and deployment of health information systems (HIS). External and internal determinants aligning with the global course influence their deployments. At the epic is digitalization, which generates endless data that has permeated healthcare. The continuous proliferation of complex and dynamic healthcare data is the digitalization frontier in healthcare that necessitates attention.Objective: This study explores the existing body of information on HIS for healthcare through the data lens to present a data-driven paradigm for healthcare augmentation paramount to attaining a sustainable and resilient HIS. Method: Preferred Reporting Items for Systematic Reviews and Meta-Analyses: PRISMA-compliant in-depth literature review was conducted systematically to synthesize and analyze the literature content to ascertain the value disposition of HIS data in healthcare delivery.Results: This study details the aspects of a data-driven paradigm for robust and sustainable HIS for health care applications. Data source, data action and decisions, data sciences techniques, serialization of data sciences techniques in the HIS, and data insight implementation and application are data-driven features expounded. These are essential data-driven paradigm building blocks that need iteration to succeed. Discussions: Existing literature considers insurgent data in healthcare challenging, disruptive, and potentially revolutionary. This view echoes the current healthcare quandary of good and bad data availability. Thus, data-driven insights are essential for building a resilient and sustainable HIS. People, technology, and tasks dominated prior HIS frameworks, with few data-centric facets. Improving healthcare and the HIS requires identifying and integrating crucial data elements. Conclusion:The paper presented a data-driven paradigm for a resilient and sustainable HIS. The findings show that data-driven track and components are essential to improve healthcare using data analytics insights. It provides an integrated footing for data analytics to support and effectively assist health care delivery.

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Section: A Data-driven Paradigm For Resilient and Sustainable His For...mentioning

confidence: 99%

A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications

Epizitone,

Moyane,

Agbehadji

2023

JMDH

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“…By introducing big data technology, multi-dimensional data analysis, realtime monitoring and analysis, as well as establishing a comprehensive data security management system, encrypting and backing up data, strengthening network and system security, and establishing emergency response mechanisms [29]. It can help enterprises better cope with the challenges in the big data environment, improve their competitiveness and sustainable development capabilities [30].…”

Section: Related Workmentioning

confidence: 99%

A Big Data Stream-Driven Risk Recognition Approach for Hospital Accounting Management Systems

Wang,

Liang,

Wang

et al. 2023

IEEE Access

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This work is confronted with hospital accounting management systems where business volume is usually large and trivial. While designing system prototype and processing algorithms, it is required to integrate realistic big data stream as the main factors for consideration. Because of such point, currently, there still lacks mature solutions for accounting risk recognition in such scenes. Combined with the micro service management technology of data flow, this paper puts forward the risk identification mode and cloud Data integrity verification algorithm for the purpose. Compared with traditional single user authentication techniques, this method has a significantly higher accuracy in hospital data analysis compared to comparative algorithms. At the same time, its error has been reduced. The multi-user parallel authentication algorithm further improves the computational efficiency of the authentication process while ensuring the integrity of data files and reducing the average time. Finally, we also make some empirical analysis on realistic data to testify performance of the proposed technical framework. The results show that the proposal is well suitable for digital risk recognition in hospital accounting management systems. And the recognition accuracy of the proposal can achieve 98%, and is about 22% higher than comparison methods.

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“…For information producers, the material they create can easily be submerged in the data of the Internet and cannot distinguish themselves from others. Much information is available to users, and separating the helpful information is challenging, wasting time and energy [3]. Universities and art colleges are particularly affected by this issue, where educators have access to abundant information resources and must exert significant effort to select only those appropriate for their teaching.…”

Section: Introductionmentioning

confidence: 99%

Animation Media Art Teaching Design Based on Big Data Fusion Technology

Wang,

Tao

2024

IJACSA

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Animation, as an ancient art expression form, still has vigorous development, and the need for animation talents in society is increasing daily. This study first introduces the definition of animation and the development of animation at home and abroad. After that, the classification regression tree algorithm's principle and function theorem are described. This study divides the data into original and new animations based on big data fusion technology. It establishes a media art teaching system with search, recommendation, and playback as the three cores. Additionally, iteration is used to calculate the optimal hidden semantic matrix, a comparison is made between the benefits and drawbacks of the Sigmoid, Tanh, and ReLU functions, and lastly, the activation function chosen is the ReLU function. Compared with the loss value in the ideal case, the experimental findings comply with the likely criteria, and the categorical regression tree algorithm model predicts an error rate that falls within acceptable limits. Practically speaking, it is known that when the hidden factor dimension is 12, the system model works best for characterizing animation features. The comparison shows that the non-standard collaborative filtering recommendation system is inferior to the recommendations filtered by the categorical regression tree algorithm model. Following the use of the system, the students' drawing and directing abilities, animation scope, and animation appreciation level all improved significantly. The questionnaire survey concluded that the teachers and students of animation majors in universities were satisfied with the system.

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CEECP: CT-based enhanced e-clinical pathways in terms of processing time to enable big data analytics in healthcare along with cloud computing

Cited by 5 publications

References 42 publications

A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications

A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications

A Big Data Stream-Driven Risk Recognition Approach for Hospital Accounting Management Systems

Animation Media Art Teaching Design Based on Big Data Fusion Technology

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