Systematic reviews of machine learning in healthcare: a literature review

Kolasa, Katarzyna; Admassu, Bisrat; Hołownia-Voloskova, Malwina; Kędzior, Katarzyna J; Poirrier, Jean-Etienne; Perni, Stefano

doi:10.1080/14737167.2023.2279107

Cited by 7 publications

(3 citation statements)

References 327 publications

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“…This review utilized a hybrid approach, employing a systematic method to gather research on stingless bee products and machine learning advancements in accordance with the guidelines outlined by Munn et al, Peters et al,. The review followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), incorporating the four-phase flow diagram suggested [11,12]. The guiding phrase of "Stingless Bee Products and Machine Learning Advancements" was applied in this review.…”

Section: Methodsmentioning

confidence: 99%

Innovative Hive Intelligence: Stingless Bee Products and Machine Learning Advancements in Agriculture and Medicine - A Review

Joseph Louis Michael Calangian Ramos,

Judy Lhyn Porlaje Sarmiento,

Edwin Romeroso Arboleda

2024

World J. Adv. Res. Rev.

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This literature review aims to explore the many applications of stingless bee products and advancements in machine learning related to stingless bees. The review includes research conducted from 2019 to 2023, with a primary focus on two key topics: the application of bee products and machine learning advancements. The review examines the various applications of stingless bee products, such as honey and bee bread, highlighting their potential in areas like medicine and anti-cancer treatments. Additionally, it examines modern machine learning advancements, including IoT systems and neural networks. The review adopts a hybrid approach, utilizing a systematic method to gather research on both stingless bee products and machine learning advancements. A total of 65 studies contributed to this research, revealing that a majority were conducted in Asia, with the genus of stingless bee Meliponini serving as the primary subject. Findings from these studies offer valuable insights for researchers, policymakers, and stakeholders interested in the sustainable utilization of stingless bee products and the integration of machine learning technologies in this domain.

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Section: Methodsmentioning

confidence: 99%

Innovative Hive Intelligence: Stingless Bee Products and Machine Learning Advancements in Agriculture and Medicine - A Review

Joseph Louis Michael Calangian Ramos,

Judy Lhyn Porlaje Sarmiento,

Edwin Romeroso Arboleda

2024

World J. Adv. Res. Rev.

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“…The accumulated published papers have undergone thorough screening, enabling a centralized focus on the review objectives via the PRISMA methodology-Preferred Reporting Item for Systems Review and Meta-Analyses [17]. The filtration process involved discerning sets of literature integrated with machine learning for fault identification and localization within an optical fiber network.…”

Section: Methodsmentioning

confidence: 99%

“…Shown in Figure 1 is the PRISMA-based assessment system [17] of the review. The evaluation of selected ML-based systems progressed with 350 studies, with 300 duplicate copies rejected from academic repositories-IEEE Xplore, ResearchGate, and Google Scholar during the preliminary search.…”

Section: Flow Of Ml-based Anomaly Localization In Optical Fiber Netwo...mentioning

confidence: 99%

Machine Learning Applications for Fault Tracing and Localization in Optical Fiber Communication Networks: A Review

Abdula,

Llagas,

Fernandez

et al. 2024

Preprint

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The review aims to assess fifteen (15) academic literature sources, highlighting the application of machine learning algorithms in the maintenance operations of optical fiber networks. It exhibits the collection of data using PRISMA methodology—Preferred Reporting Item for Systems Review and Meta-Analyses. The application, results, and performance metrics are discussed based on the collected observations, computations, and statistics in the studies, which revealed records of high accuracy degrees ranging from 86% to 98% on average and quality ML models including Neural Networks (NNs), Support Vector Machines (SVMs), and LSTM, as well as deep learning models that disclosed effective results of determining challenges and problems within the optical fiber lines. The review mainly centralized on superior machine learning technologies that surpass traditional techniques in fault detection and localization for improved optical fiber networks’ operations while providing insights into the limitations and challenges encountered in real-world applications of these models, offering a comprehensive perspective on the optical fiber network’s domain.

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Embryonic Machine-Deep Learning, Smart Healthcare and Privacy Deliberations in Hospital Industry: Lensing Confidentiality of Patient’s Information and Personal Data in Legal-Ethical Landscapes Projecting Futuristic Dimensions

Singh,

Kaunert

2024

Engineering Cyber-Physical Systems and Critical Infrastructures

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Systematic reviews of machine learning in healthcare: a literature review

Cited by 7 publications

References 327 publications

Innovative Hive Intelligence: Stingless Bee Products and Machine Learning Advancements in Agriculture and Medicine - A Review

Innovative Hive Intelligence: Stingless Bee Products and Machine Learning Advancements in Agriculture and Medicine - A Review

Machine Learning Applications for Fault Tracing and Localization in Optical Fiber Communication Networks: A Review

Embryonic Machine-Deep Learning, Smart Healthcare and Privacy Deliberations in Hospital Industry: Lensing Confidentiality of Patient’s Information and Personal Data in Legal-Ethical Landscapes Projecting Futuristic Dimensions

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