Multi-domain and multi-view networks model for clustering hospital admissions from the emergency department

Albarakati, Nouf; Obradović, Zoran

doi:10.1007/s41060-018-0147-5

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…4 Regression algorithm procedures similarities in the same group and with dissimilarities in other groups (Iglesias et al, 2021;Shalev-Shwartz & Ben-David, 2014). This procedure is very useful in engineering, health science, humanities, economics, and other areas (Albarakati & Obradovic, 2019;Azevedo et al, 2022;Bi et al, 2020;Chen et al, 2020;Zhou et al, 2019).…”

Section: Clustering Methodsmentioning

confidence: 99%

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Azevedo,

Rocha,

Pereira

2024

Mach Learn

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Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.

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Section: Clustering Methodsmentioning

confidence: 99%

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Azevedo,

Rocha,

Pereira

2024

Mach Learn

View full text Add to dashboard Cite

show abstract

“…A typical ECD management framework for managing natural hazards and health emergencies consists of four phases: mitigation, preparedness, response, and recovery (MPRR) [ 4 ]. 4 Mitigation develops structural and nonstructural disaster management measures and resources, preventive regulations and strategies, advanced codes and standards to prevent and contain ECD consequences and impacts.…”

Section: From Ecd Management To Resiliencementioning

confidence: 99%

AI and data science for smart emergency, crisis and disaster resilience

Cao

2023

Int J Data Sci Anal

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“…Finally, the fifth accepted paper extended [5] their preliminary work, focused on hospital clustering analysis to study hospital monthly admission behavior for different diseases. In this work, they included other aspects of disease data and the combination of different views of disease data.…”

Section: The Five Papers Selected For This Special Issuementioning

confidence: 99%

Introduction to the special issue on social data analytics in medicine and healthcare

Rodríguez‐González

Vakali

Mayer

et al. 2019

Int J Data Sci Anal

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Multi-domain and multi-view networks model for clustering hospital admissions from the emergency department

Cited by 9 publications

References 16 publications

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Hybrid approaches to optimization and machine learning methods: a systematic literature review

AI and data science for smart emergency, crisis and disaster resilience

Introduction to the special issue on social data analytics in medicine and healthcare

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