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Network Analytics in Healthcare Decision Making

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Communication and Informatics".

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 31785

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Dr. Shahadat Uddin

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School of Project Management, Faculty of Engineering, The University of Sydney, Darlington, NSW 2008, Australia
Interests: health informatics; artificial intelligence; data science; complex networks; project analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite papers to this Special Issue of the International Journal of Environmental Research and Public Health, which will explore the application of different measures, methods and models of network analytics to the decision-making process of healthcare systems.
The principal goal of a well-structured healthcare system is to provide the best possible care to its consumers. Various healthcare stakeholders including general practitioners, specialists, hospitals, radiology and image providers, pathology service providers, pharmacies, aged care facilities and funders work together to keep the whole healthcare system running smoothly. These healthcare entities generate a large amount of data, which become valuable resources that help in making evidence-based decisions and understanding how the whole system is performing. Since these entities and the data they generate are inherently connected, network analytics has a significant potential to offer insights into the hidden relationships across these data elements, which can eventually facilitate the process of healthcare decision-making procedure. Due to their strong potential in revealing hidden insights of any networks, the measures (e.g., network centrality, centralization and density), methods (e.g., community detection, sub-group and core-periphery analysis) and models (e.g., exponential random graph and structural equivalence) of network analytics have gained wide acceptability in healthcare research in recent years.
In this Special Issue, we welcome the submission of methodological, empirical and review papers that use methods, measures and models of data analytics and have a clear implication for healthcare decision-making. The submitted papers can be based on primary (e.g., based on study design) and/or secondary research data (e.g., administrative claim data and electronic medical records).
Papers of a high academic standard addressing any healthcare decision-making issues using network analytics are invited for submission to this Special Issue.

Dr. Shahadat Uddin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

Network analysis, visualization and comparison
Healthcare collaboration and coordination
Professional network in healthcare
Disease comorbidity and disease network
Health and healthcare trajectory
Healthcare policy

Published Papers (9 papers)

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Research

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21 pages, 1546 KiB

Open AccessArticle

Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma

by Md. Ali Hossain, Tania Akter Asa, Md. Mijanur Rahman, Shahadat Uddin, Ahmed A. Moustafa, Julian M. W. Quinn and Mohammad Ali Moni

Int. J. Environ. Res. Public Health 2020, 17(4), 1373; https://doi.org/10.3390/ijerph17041373 - 20 Feb 2020

Cited by 17 | Viewed by 3718

Abstract

Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis. Full article

(This article belongs to the Special Issue Network Analytics in Healthcare Decision Making)

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25 pages, 2753 KiB

Open AccessArticle

A Network-Based Bioinformatics Approach to Identify Molecular Biomarkers for Type 2 Diabetes that Are Linked to the Progression of Neurological Diseases

by Md Habibur Rahman, Silong Peng, Xiyuan Hu, Chen Chen, Md Rezanur Rahman, Shahadat Uddin, Julian M.W. Quinn and Mohammad Ali Moni

Int. J. Environ. Res. Public Health 2020, 17(3), 1035; https://doi.org/10.3390/ijerph17031035 - 06 Feb 2020

Cited by 51 | Viewed by 5822

Abstract

Neurological diseases (NDs) are progressive disorders, the progression of which can be significantly affected by a range of common diseases that present as comorbidities. Clinical studies, including epidemiological and neuropathological analyses, indicate that patients with type 2 diabetes (T2D) have worse progression of NDs, suggesting pathogenic links between NDs and T2D. However, finding causal or predisposing factors that link T2D and NDs remains challenging. To address these problems, we developed a high-throughput network-based quantitative pipeline using agnostic approaches to identify genes expressed abnormally in both T2D and NDs, to identify some of the shared molecular pathways that may underpin T2D and ND interaction. We employed gene expression transcriptomic datasets from control and disease-affected individuals and identified differentially expressed genes (DEGs) in tissues of patients with T2D and ND when compared to unaffected control individuals. One hundred and ninety seven DEGs (99 up-regulated and 98 down-regulated in affected individuals) that were common to both the T2D and the ND datasets were identified. Functional annotation of these identified DEGs revealed the involvement of significant cell signaling associated molecular pathways. The overlapping DEGs (i.e., seen in both T2D and ND datasets) were then used to extract the most significant GO terms. We performed validation of these results with gold benchmark databases and literature searching, which identified which genes and pathways had been previously linked to NDs or T2D and which are novel. Hub proteins in the pathways were identified (including DNM2, DNM1, MYH14, PACSIN2, TFRC, PDE4D, ENTPD1, PLK4, CDC20B, and CDC14A) using protein-protein interaction analysis which have not previously been described as playing a role in these diseases. To reveal the transcriptional and post-transcriptional regulators of the DEGs we used transcription factor (TF) interactions analysis and DEG-microRNAs (miRNAs) interaction analysis, respectively. We thus identified the following TFs as important in driving expression of our T2D/ND common genes: FOXC1, GATA2, FOXL1, YY1, E2F1, NFIC, NFYA, USF2, HINFP, MEF2A, SRF, NFKB1, USF2, HINFP, MEF2A, SRF, NFKB1, PDE4D, CREB1, SP1, HOXA5, SREBF1, TFAP2A, STAT3, POU2F2, TP53, PPARG, and JUN. MicroRNAs that affect expression of these genes include mir-335-5p, mir-16-5p, mir-93-5p, mir-17-5p, mir-124-3p. Thus, our transcriptomic data analysis identifies novel potential links between NDs and T2D pathologies that may underlie comorbidity interactions, links that may include potential targets for therapeutic intervention. In sum, our neighborhood-based benchmarking and multilayer network topology methods identified novel putative biomarkers that indicate how type 2 diabetes (T2D) and these neurological diseases interact and pathways that, in the future, may be targeted for treatment. Full article

(This article belongs to the Special Issue Network Analytics in Healthcare Decision Making)

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17 pages, 2233 KiB

Open AccessArticle

A Framework to Understand the Progression of Cardiovascular Disease for Type 2 Diabetes Mellitus Patients Using a Network Approach

by Md Ekramul Hossain, Shahadat Uddin, Arif Khan and Mohammad Ali Moni

Int. J. Environ. Res. Public Health 2020, 17(2), 596; https://doi.org/10.3390/ijerph17020596 - 16 Jan 2020

Cited by 17 | Viewed by 3328

Abstract

The prevalence of chronic disease comorbidity has increased worldwide. Comorbidity—i.e., the presence of multiple chronic diseases—is associated with adverse health outcomes in terms of mobility and quality of life as well as financial burden. Understanding the progression of comorbidities can provide valuable insights towards the prevention and better management of chronic diseases. Administrative data can be used in this regard as they contain semantic information on patients’ health conditions. Most studies in this field are focused on understanding the progression of one chronic disease rather than multiple diseases. This study aims to understand the progression of two chronic diseases in the Australian health context. It specifically focuses on the comorbidity progression of cardiovascular disease (CVD) in patients with type 2 diabetes mellitus (T2DM), as the prevalence of these chronic diseases in Australians is high. A research framework is proposed to understand and represent the progression of CVD in patients with T2DM using graph theory and social network analysis techniques. Two study cohorts (i.e., patients with both T2DM and CVD and patients with only T2DM) were selected from an administrative dataset obtained from an Australian health insurance company. Two baseline disease networks were constructed from these two selected cohorts. A final disease network from two baseline disease networks was then generated by weight adjustments in a normalized way. The prevalence of renal failure, fluid and electrolyte disorders, hypertension and obesity was significantly higher in patients with both CVD and T2DM than patients with only T2DM. This showed that these chronic diseases occurred frequently during the progression of CVD in patients with T2DM. The proposed network-based model may potentially help the healthcare provider to understand high-risk diseases and the progression patterns between the recurrence of T2DM and CVD. Also, the framework could be useful for stakeholders including governments and private health insurers to adopt appropriate preventive health management programs for patients at a high risk of developing multiple chronic diseases. Full article

(This article belongs to the Special Issue Network Analytics in Healthcare Decision Making)

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14 pages, 360 KiB

Open AccessArticle

VIKOR Method for MAGDM Based on Q-Rung Interval-Valued Orthopair Fuzzy Information and Its Application to Supplier Selection of Medical Consumption Products

by Hui Gao, Linggang Ran, Guiwu Wei, Cun Wei and Jiang Wu

Int. J. Environ. Res. Public Health 2020, 17(2), 525; https://doi.org/10.3390/ijerph17020525 - 14 Jan 2020

Cited by 90 | Viewed by 3457

Abstract

The VIKOR model has been considered a viable tool for many decision-making applications in the past few years, given the advantages of considering the compromise between maximizing the utility of group and minimizing personal regrets. The q-rung interval-valued orthopair fuzzy set (q-RIVOFS) is a generalization of intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS) and has emerged to solve more complex and uncertain decision making problems which IFS and PFS cannot handle. In this manuscript, the key innovation is to combine the traditional VIKOR model with q-RIVOFS to develop the q-rung interval-valued orthopair fuzzy VIKOR model. In the new developed model, to express more information, the attribute’s values in MAGDM problems are depicted by q-RIVOFNs. First of all, some basic theories and aggregation operators of q-RIVOFNs are simply introduced. Then we develop the origin VIKOR model to q-RIVOFS environment and briefly express the computing steps of this new established model. Thereafter, the effectiveness of the model is verified by an example of supplier selection of medical consumer products and through comparative analysis, the superiority of the new method is further illustrated. Full article

(This article belongs to the Special Issue Network Analytics in Healthcare Decision Making)

10 pages, 1626 KiB

Open AccessArticle

Potential Confounders in the Analysis of Brazilian Adolescent’s Health: A Combination of Machine Learning and Graph Theory

by Amanda Yumi Ambriola Oku, Guilherme Augusto Zimeo Morais, Ana Paula Arantes Bueno, André Fujita and João Ricardo Sato

Int. J. Environ. Res. Public Health 2020, 17(1), 90; https://doi.org/10.3390/ijerph17010090 - 21 Dec 2019

Cited by 2 | Viewed by 3466

Abstract

The prevalence of health problems during childhood and adolescence is high in developing countries such as Brazil. Social inequality, violence, and malnutrition have strong impact on youth health. To better understand these issues we propose to combine machine-learning methods and graph analysis to build predictive networks applied to the Brazilian National Student Health Survey (PenSE 2015) data, a large dataset that consists of questionnaires filled by the students. By using a combination of gradient boosting machines and centrality hub metric, it was possible to identify potential confounders to be considered when conducting association analyses among variables. The variables were ranked according to their hub centrality to predict the other variables from a directed weighted-graph perspective. The top five ranked confounder variables were “gender”, “oral health care”, “intended education level”, and two variables associated with nutrition habits—“eat while watching TV” and “never eat fast-food”. In conclusion, although causal effects cannot be inferred from the data, we believe that the proposed approach might be a useful tool to obtain novel insights on the association between variables and to identify general factors related to health conditions. Full article

(This article belongs to the Special Issue Network Analytics in Healthcare Decision Making)

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15 pages, 376 KiB

Open AccessArticle

Supplier Selection of Medical Consumption Products with a Probabilistic Linguistic MABAC Method

by Guiwu Wei, Cun Wei, Jiang Wu and Hongjun Wang

Int. J. Environ. Res. Public Health 2019, 16(24), 5082; https://doi.org/10.3390/ijerph16245082 - 12 Dec 2019

Cited by 71 | Viewed by 2926

Abstract

In order to obtain an optimal medical consumption product supplier, the integration of combined weights and multi-attributive border approximation area comparison (MABAC) under probabilistic linguistic sets (PLTSs) has offered a novel integrated model in which the CRiteria Importance Through Intercriteria Correlation (CRITIC) method is employed for calculating the objective weights of various attributes and the MABAC method with PLTSs is used to acquire the final ranking result of a medical consumption product supplier. Additionally, so as to indicate the applicability of the devised method, this model is confirmed by a numerical case for the supplier selection of medical consumption products. Some comparative studies are made with some existing methods. The proposed method can also successfully select suitable alternatives in other selection problems. Full article

(This article belongs to the Special Issue Network Analytics in Healthcare Decision Making)

14 pages, 667 KiB

Open AccessArticle

Pythagorean 2-Tuple Linguistic Taxonomy Method for Supplier Selection in Medical Instrument Industries

by Tingting He, Guiwu Wei, Jianping Lu, Cun Wei and Rui Lin

Int. J. Environ. Res. Public Health 2019, 16(23), 4875; https://doi.org/10.3390/ijerph16234875 - 03 Dec 2019

Cited by 55 | Viewed by 2779

Abstract

Supplier selection in medical instrument industries is a classical multiple attribute group decision making (MAGDM) problem. The Pythagorean 2-tuple linguistic sets (P2TLSs) can reflect uncertain or fuzzy information well and solve the supplier selection in medical instrument industries, and the original Taxonomy is very appropriate for comparing different alternatives with respect to their advantages from studied attributes. In this study, we present an algorithm that combines Pythagorean 2-tuple linguistic numbers (P2TLNs) with the Taxonomy method, where P2TLNs are applied to express the evaluation of decision makers on alternatives. Relying on the Pythagorean 2-tuple linguistic weighted average (P2TLWA) operator or Pythagorean 2-tuple linguistic weighted geometric (P2TLWG) operator to fuse P2TLNs, the new general framework is established for Pythagorean 2-tuple linguistic multiple attribute group decision making (MAGDM) under the classical Taxonomy method. Ultimately, an application case for supplier selection in medical instrument industries is designed to test the novel method’s applicability and practicality and a comparative analysis with three other methods is used to elaborate further. Full article

(This article belongs to the Special Issue Network Analytics in Healthcare Decision Making)

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23 pages, 2203 KiB

Open AccessArticle

Healthcare Supply Chain Network Coordination Through Medical Insurance Strategies with Reference Price Effect

by Lingyu Gao and Xiaoli Wang

Int. J. Environ. Res. Public Health 2019, 16(18), 3479; https://doi.org/10.3390/ijerph16183479 - 18 Sep 2019

Cited by 4 | Viewed by 3195

Abstract

China has established the universal medical insurance system and individual out of pocket costs have decreased, however, the average healthcare expenditure of the Chinese population and the expenses of the whole society have increased substantially. One major challenge which impedes the progress of attaining sustainable development of the social healthcare system in China is that the number of hospital admissions is disproportionate. Superior hospitals are overcrowded, whereas subordinate hospitals are experiencing low admissions. In this paper, we apply the game theory model to coordinate the healthcare supply chain network, which is composed of the government, medical insurance fund, superior hospitals, subordinate hospitals and patients. Especially by taking the reference price effect into account, this paper analyzes different medical insurance reimbursement strategies and their influence on patient choice and the healthcare supply chain network. The result shows that the reference price effect increases the leverage of medical insurance, guides patients’ choice, optimizes the allocation of medical resources and reduces the medical expends. In comparison to a decentralized decision- making strategy, a centralized decision- making strategy can stimulate both superior hospital and subordinate hospital’s cooperative intentions which benefits the social healthcare system. Full article

(This article belongs to the Special Issue Network Analytics in Healthcare Decision Making)

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Review

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20 pages, 1122 KiB

Open AccessFeature PaperReview

A Systematic Review of Network Studies Based on Administrative Health Data

by Shakir Karim, Shahadat Uddin, Tasadduq Imam and Mohammad Ali Moni

Int. J. Environ. Res. Public Health 2020, 17(7), 2568; https://doi.org/10.3390/ijerph17072568 - 09 Apr 2020

Cited by 4 | Viewed by 2498

Abstract

Effective and efficient delivery of healthcare services requires comprehensive collaboration and coordination between healthcare entities and their complex inter-reliant activities. This inter-relation and coordination lead to different networks among diverse healthcare stakeholders. It is important to understand the varied dynamics of these networks to measure the efficiency of healthcare delivery services. To date, however, a work that systematically reviews these networks outlined in different studies is missing. This article provides a comprehensive summary of studies that have focused on networks and administrative health data. By summarizing different aspects including research objectives, key research questions, adopted methods, strengths and weaknesses, this research provides insights into the inherently complex and interlinked networks present in healthcare services. The outcome of this research is important to healthcare management and may guide further research in this area. Full article

(This article belongs to the Special Issue Network Analytics in Healthcare Decision Making)

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