Bayesian exponential random graph modelling of interhospital patient referral networks

A Bayesian multilevel model for populations of networks using exponential-family random graphs

The collection of data on populations of networks is becoming increasingly common, where each data point can be seen as a realisation of a network-valued random variable. Moreover, each data point may be accompanied by some additional covariate information and one may be interested in assessing the effect of these covariates on network structure within the population. A canonical example is that of brain networks: a typical neuroimaging study collects one or more brain scans across multiple individuals, each of which can be modelled as a network with nodes corresponding to distinct brain regions and edges corresponding to structural or functional connections between these regions. Most statistical network models, however, were originally proposed to describe a single underlying relational structure, although recent years have seen a drive to extend these models to populations of networks. Here, we describe a model for when the outcome of interest is a network-valued random variable whose distribution is given by an exponential random graph model. To perform inference, we implement an exchange-within-Gibbs MCMC algorithm that generates samples from the doubly-intractable posterior. To illustrate this approach, we use it to assess population-level variations in networks derived from fMRI scans, enabling the inference of age- and intelligence-related differences in the topological structure of the brain's functional connectivity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11222-024-10446-0.

Solving patient referral problems by using bat algorithm

BACKGROUND:

A two-hospital patient referral problem intends to calculate an optimal value of referral patients between two hospitals and to evaluate whether or not the current number of referral patients is too low.

OBJECTIVE:

The goal of this study is to develop a simulation-based optimization algorithm to find the optimal referral between two hospitals with the unfixed daily patient referral policy.

METHODS:

This study applied system simulation and a bat algorithm (BA) to build a simulation model in accordance with the status of the two hospitals case and to calculate an optimal value of daily referral patients.

RESULTS:

Based on the 20 test instances, we verified the stability of this algorithm. The results show that the average magnetic resonance imaging (MRI) patient wait time reduced from 16 days to eight days. The hospital should increase the average total monthly MRI referral patients to 370 under the limitation of the daily referral patients to 25.

CONCLUSIONS:

This research investigated the two-hospital patient referral problems. We conducted and analyzed a simulation model and improved the case hospital’s conditions, enhancing the quality of its medical care. The findings of this study can extend to other departments or hospitals.

Patent citation network analysis: A perspective from descriptive statistics and ERGMs

Patent Citation Analysis has been gaining considerable traction over the past few decades. In this paper, we collect extensive information on patents and citations and provide a perspective of citation network analysis of patents from a statistical viewpoint. We identify and analyze the most cited patents, the most innovative and the highly cited companies along with the structural properties of the network by providing in-depth descriptive analysis. Furthermore, we employ Exponential Random Graph Models (ERGMs) to analyze the citation networks. ERGMs enables understanding the social perspectives of a patent citation network which has not been studied earlier. We demonstrate that social properties such as homophily (the inclination to cite patents from the same country or in the same language) and transitivity (the inclination to cite references' references) together with the technicalities of the patents (e.g., language, categories), has a significant effect on citations. We also provide an in-depth analysis of citations for sectors in patents and how it is affected by the size of the same. Overall, our paper delves into European patents with the aim of providing new insights and serves as an account for fitting ERGMs on large networks and analyzing them. ERGMs help us model network mechanisms directly, instead of acting as a proxy for unspecified dependence and relationships among the observations.

Exploiting Inter-Organizational Relationships in Health Care: A Bibliometric Analysis and Literature Review

Inter-organizational relationships are high on the health policy agenda. Scholars and practitioners have provided heterogeneous views about the triggers of collaborative practices and the success factors that underpin the sustainability of inter-organizational relationships in the health care domain. The article proposes a literature review aimed at systematizing current scientific research that contextualizes inter-organizational relationships to health care. A mixed approach was undertaken, which consisted of a bibliometric analysis followed by a narrative literature review. A tailored search strategy on Elsevier’s Scopus yielded 411 relevant records, which were carefully screened for inclusion in this study. After screening, 105 papers were found to be consistent with the study purposes and included in this literature review. The findings emphasize that the establishment and implementation of inter-organizational relationships in health care are affected by several ambiguities, which concern both the governance and the structuring of collaborative relationships. The viability and the success of inter-organizational relationships depend on the ability of both central and peripheral partners to acknowledge and address such ambiguities. Failure to do so involves an opportunistic participation to inter-organizational relationships. This endangers conflicting behaviors rather than collaboration among partners.

A survey on exponential random graph models: an application perspective

The uncertainty underlying real-world phenomena has attracted attention toward statistical analysis approaches. In this regard, many problems can be modeled as networks. Thus, the statistical analysis of networked problems has received special attention from many researchers in recent years. Exponential Random Graph Models, known as ERGMs, are one of the popular statistical methods for analyzing the graphs of networked data. ERGM is a generative statistical network model whose ultimate goal is to present a subset of networks with particular characteristics as a statistical distribution. In the context of ERGMs, these graph's characteristics are called statistics or configurations. Most of the time they are the number of repeated subgraphs across the graphs. Some examples include the number of triangles or the number of cycle of an arbitrary length. Also, any other census of the graph, as with the edge density, can be considered as one of the graph's statistics. In this review paper, after explaining the building blocks and classic methods of ERGMs, we have reviewed their newly presented approaches and research papers. Further, we have conducted a comprehensive study on the applications of ERGMs in many research areas which to the best of our knowledge has not been done before. This review paper can be used as an introduction for scientists from various disciplines whose aim is to use ERGMs in some networked data in their field of expertise.