Community Detection Algorithms in Healthcare Applications: A Systematic Review

Rostami, Mehrdad; Oussalah, Mourad; Berahmand, Kamal; Farrahi, Vahid

doi:10.1109/access.2023.3260652

Cited by 55 publications

(6 citation statements)

References 229 publications

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“…Some graph theory methods have already emerged in the medical field. Rostami et al [ 29 ] introduced the application of community detection algorithms in the healthcare domain, and Azadifar et al [ 30 ] proposed a graph-based gene selection method for cancer diagnosis. It is important to emphasize that graph theory methods and deep learning methods are not mutually exclusive but can be combined and integrated.…”

Section: Discussionmentioning

confidence: 99%

HAHNet: a convolutional neural network for HER2 status classification of breast cancer

Wang,

Zhu,

Chen

et al. 2023

BMC Bioinformatics

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Objective Breast cancer is a significant health issue for women, and human epidermal growth factor receptor-2 (HER2) plays a crucial role as a vital prognostic and predictive factor. The HER2 status is essential for formulating effective treatment plans for breast cancer. However, the assessment of HER2 status using immunohistochemistry (IHC) is time-consuming and costly. Existing computational methods for evaluating HER2 status have limitations and lack sufficient accuracy. Therefore, there is an urgent need for an improved computational method to better assess HER2 status, which holds significant importance in saving lives and alleviating the burden on pathologists. Results This paper analyzes the characteristics of histological images of breast cancer and proposes a neural network model named HAHNet that combines multi-scale features with attention mechanisms for HER2 status classification. HAHNet directly classifies the HER2 status from hematoxylin and eosin (H&E) stained histological images, reducing additional costs. It achieves superior performance compared to other computational methods. Conclusions According to our experimental results, the proposed HAHNet achieved high performance in classifying the HER2 status of breast cancer using only H&E stained samples. It can be applied in case classification, benefiting the work of pathologists and potentially helping more breast cancer patients.

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Section: Discussionmentioning

confidence: 99%

HAHNet: a convolutional neural network for HER2 status classification of breast cancer

Wang,

Zhu,

Chen

et al. 2023

BMC Bioinformatics

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“…Additionally, community detection in social networks has emerged as a pivotal research area, and over the past decade, a plethora of methodologies and algorithms have been proposed driven by the need to unveil the underlying network structures and topologies for studying their influence on information spreading or on other network dynamics. These methods range from traditional graph theoretic approaches, such as modularity optimization and spectral clustering, to more recent advancements like deep learning-based techniques [43]. Notably, the choice of a community detection method depends on the specific characteristics of the social network under investigation, including its size, sparsity, and the nature of the relationships among its nodes.…”

Section: Real-time Insights Identify Influential Usersmentioning

confidence: 99%

Analyzing the Spread of Misinformation on Social Networks: A Process and Software Architecture for Detection and Analysis

Duzen,

Riveni,

Aktas

2023

Computers

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The rapid dissemination of misinformation on social networks, particularly during public health crises like the COVID-19 pandemic, has become a significant concern. This study investigates the spread of misinformation on social network data using social network analysis (SNA) metrics, and more generally by using well known network science metrics. Moreover, we propose a process design that utilizes social network data from Twitter, to analyze the involvement of non-trusted accounts in spreading misinformation supported by a proof-of-concept prototype. The proposed prototype includes modules for data collection, data preprocessing, network creation, centrality calculation, community detection, and misinformation spreading analysis. We conducted an experimental study on a COVID-19-related Twitter dataset using the modules. The results demonstrate the effectiveness of our approach and process steps, and provides valuable insight into the application of network science metrics on social network data for analysing various influence-parameters in misinformation spreading.

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“…On the other hand, patients go to different healthcare service providers during their lives, resulting in widespread e-health records among many independent repositories, each one maintained by a different healthcare organization. Consequently, a technology able to improve e-health record sharing and exchange among healthcare organizations [1][2][3] represents a need for the healthcare domain but also a challenge in the research community because several security and privacy problems arise in this new setting. Health data are sensitive [4,5], and their access should be granted only to authorized entities [6,7].…”

Section: Introductionmentioning

confidence: 99%

Enabling secure health information sharing among healthcare organizations by public blockchain

Lax,

Nardone,

Russo

2024

Multimed Tools Appl

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The facilitation of sharing and exchanging patients’ health records is a paramount opportunity in e-health, enabling healthcare providers to garner a comprehensive and clear perspective of patients’ medical histories without necessitating direct inquiries. Besides this great advantage, it introduces substantial issues on security and privacy, mainly related to unauthorized access to e-health records when different healthcare service providers maintain records. In this paper, we deal with this problem and propose using the blockchain technology (1) to obfuscate the linkage between patients’ identities and their e-health records and (2) to grant access to e-health records exclusively to entities authorized by patients themselves. Key outcomes include using a digital identity based on the Electronic Identification, Authentication, and Trust Services Regulation (eIDAS) to control access to these records, and a concrete implementation by adopting the Ethereum blockchain. Our solution relies on using a public blockchain, which is an improvement for the state of the art, in which only private or consortium blockchains have been proposed. The resulting solution has been analyzed, and the effectiveness and affordability of the proposal have been shown.

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Community Detection Algorithms in Healthcare Applications: A Systematic Review

Cited by 55 publications

References 229 publications

HAHNet: a convolutional neural network for HER2 status classification of breast cancer

HAHNet: a convolutional neural network for HER2 status classification of breast cancer

Analyzing the Spread of Misinformation on Social Networks: A Process and Software Architecture for Detection and Analysis

Enabling secure health information sharing among healthcare organizations by public blockchain

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