# A Visual Analytics Framework for Inter-Hospital Transfer Network of Stroke Patients
## 2 Related Work  
The patient transfer was visually analyzed with node-link diagrams for various clinical purposes. [PERSON] et al. [8] analyzed the spread of pathogens in the patient transfer network in the United States. The network structure constructed based on patient transfers for two years was plotted on a map with the number of transferred patients indicated by the color of the link. The analysis results were used to introduce a method for selecting sensor hospitals based on a network measure (i.e., in-degree) and for quickly detecting disease prevalence based on temporal, geographical, and topological properties. [PERSON] et al. [9] divided the patient group (1) with HAI, (2) with suspected HAI, and (3) all patients, and they derived the network connection structure based on the patient transfer pattern for determining the optimal network for studying HAIs. The transfer pattern was also presented on a map, and the authors identified notable transfer patterns that could help find a critical hospital at the regional and county level in France. The nodes in the diagram were colored according to the community derived from the detection algorithm [10]. [PERSON] et al. [11] used a community detection algorithm [12], which extends the previous approach [10] for large networks, to analyze the clustering patterns of hospitalsin China. The authors neglected geographic information in analyzing the network and plotting the nodes and links. We presented a hospital network using both geographical layout and force-directed layout algorithms for analysis of geographical relationships and topological analysis within each cluster, respectively. A study on patient transfers was conducted regarding treatments for stroke patients. [PERSON] et al. [2] studied the geographic access of stroke patients in the United States. They argued that a more efficient stroke treatment system was required by analyzing country-level data on the accessibility to hospitals and the number of patients who received treatments from the hospitals. Accessibility to treatment facilities was visualized using a choropleth map. [PERSON] et al. [7] argued that the study of networks could help improve stroke treatment systems, and they introduced examples of community-detection algorithms employed in other medical studies. It was followed by studies that grafted a network analysis on various regions. For example, [PERSON] et al. [5] analyzed stroke patient transfer data in the northeastern United States and visualized the network using ArcGIS. Each network constructed based on patient transfer data at two time-points (2007 and 2011) was expressed as a node-link diagram on a map; hospitals that sent a significant number of patients and hospitals that received them were analyzed. Another network analysis of the stroke treatment system was conducted for California [1]. The two networks constructed based on patient transfer data (between 2013 and 2014; and 2016 and 2017) were visualized on a map to examine changes from the introduction of EVT; the rate of progression of the EVT treatment and the rate of patient transfer were analyzed. Meanwhile, a study on telestroke care analyzed the hub-and-spoke structures and investigated how the network changed over time [13]. Our study analyzed the community in the network based on patient transfer data using the Louvain algorithm for directed networks [14]. We provided tailored visualization without commercial tools by identifying tasks through collaboration with medical researchers. Prior work explored methods to visualize networks based on the data size (i.e., number of nodes and links) and the purpose of visualization. [PERSON] et al. [15] classified the layout method of placing nodes and links on a screen into seven types: force-directed, hive plot, adjacency matrix, arc diagram, Sankey diagram, chord diagram, and pivot graph. They compared the number of nodes and links suitable for network visualization. [PERSON] et al. [16] conducted a survey and analyzed previous studies that visualized the group structure of graphs.