A Causal Model Using Self-Organizing Maps

2023

International Health

Background People with affective disorder–induced disabilities (ADIDs) often experience complex needs that delay their healthcare. Discovering hidden patterns in these people for real-world use of health services is essential to improve healthcare delivery. Methods A cross-sectional study population (2501 adults with ADIDs) was obtained from the Australian national representative survey of disability in 2015, including 21 demographic, health and social characteristics and healthcare delay information in general practice, specialist and hospital services. The Self-Organising Map Network was used to identify hidden risk patterns associated with healthcare delay and investigate potential predictors of class memberships by means of simple visualisations. Results While experiencing disability avoidance showed across different healthcare delays, labour force appeared not to have any influence. Approximately 30% delayed their healthcare to general practice services; these were young, single females in great need of psychosocial support and aids for personal activities. Those who delayed their healthcare commonly presented a lack of social connections and a need for contact with family or friends not living in the same household. Conclusions The pattern evidence provides an avenue to further develop integrated care strategies with better targeting of people with ADIDs, considering social participation challenges facing them, to improve health service utilisation.

Section: Methodsmentioning

confidence: 99%

Discovering risk patterns in people with affective disorder–induced disabilities associated with their healthcare delay

2023

International Health

“…Based on machine learning and information visualisation capabilities of the SOM, the SOMNet is developed for interactive visual data mining between multiple datasets [ 21 , 22 ]. The SOMNet learns the structural relationships between different datasets by its weight association.…”

Section: Methodsmentioning

confidence: 99%

“…An alternative is to combine artificial neuronal networks (ANNs) with advanced information visualisation techniques in a single DSS. Based on the demand, the aim of this study is to assess a new DSS model using a novel approach, a self-organising map network (SOMNet) [ 21 , 22 ], combined with the EbCA [ 11 ] for the use of knowledge-guided mental health planning. The SOMNet was developed to facilitate interactive visual data mining of complex data to enable domain experts to (1) generate and verify hypotheses; (2) express interest through the process of KDD; (3) enhance information transferring between analysts and decision-makers; (4) specify information processing and present outcomes of analytical reasoning processes; and (5) identify hidden information and elicit tacit knowledge that can be formalised and transformed into rules for further data analysis [ 23 – 25 ].…”

Section: Introductionmentioning

confidence: 99%

Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning

Salvador‐Carulla

Salinas-Pérez

et al. 2018

Health Res Policy Sys

Self Cite

BackgroundDecision-making in mental health systems should be supported by the evidence-informed knowledge transfer of data. Since mental health systems are inherently complex, involving interactions between its structures, processes and outcomes, decision support systems (DSS) need to be developed using advanced computational methods and visual tools to allow full system analysis, whilst incorporating domain experts in the analysis process. In this study, we use a DSS model developed for interactive data mining and domain expert collaboration in the analysis of complex mental health systems to improve system knowledge and evidence-informed policy planning.MethodsWe combine an interactive visual data mining approach, the self-organising map network (SOMNet), with an operational expert knowledge approach, expert-based collaborative analysis (EbCA), to develop a DSS model. The SOMNet was applied to the analysis of healthcare patterns and indicators of three different regional mental health systems in Spain, comprising 106 small catchment areas and providing healthcare for over 9 million inhabitants. Based on the EbCA, the domain experts in the development team guided and evaluated the analytical processes and results. Another group of 13 domain experts in mental health systems planning and research evaluated the model based on the analytical information of the SOMNet approach for processing information and discovering knowledge in a real-world context. Through the evaluation, the domain experts assessed the feasibility and technology readiness level (TRL) of the DSS model.ResultsThe SOMNet, combined with the EbCA, effectively processed evidence-based information when analysing system outliers, explaining global and local patterns, and refining key performance indicators with their analytical interpretations. The evaluation results showed that the DSS model was feasible by the domain experts and reached level 7 of the TRL (system prototype demonstration in operational environment).ConclusionsThis study supports the benefits of combining health systems engineering (SOMNet) and expert knowledge (EbCA) to analyse the complexity of health systems research. The use of the SOMNet approach contributes to the demonstration of DSS for mental health planning in practice.Electronic supplementary materialThe online version of this article (10.1186/s12961-018-0308-y) contains supplementary material, which is available to authorized users.

“…Causality is the relationship between two events, if changes of one (cause) trigger changes of the other (effect) [7]. In our previous work [2], a causal analysis model was developed for analyzing causality of multivariate and nonlinear data (unlabeled in nature). In that model, different Self-Organizing Maps (SOMs) [5] for input and output data sets were networked using a weight association based on the connection prototype feature vector similarity.…”

Section: Introductionmentioning

confidence: 99%

“…A set of neurons of the map, which are prototype feature vectors adaptively projected for original feature vectors, reflects the data properties. Using the causal analysis model in our previous work [2], a weight distribution is estimated on the property distribution of the output SOM for a given input. Fig.…”

Section: Introductionmentioning

confidence: 99%

A Visual Analysis of Changes to Weighted Self-Organizing Map Patterns

Neural Information Processing

Gudmundsson

Takatsuka

2017

Self Cite

Estimating output changes by input changes is the main task in causal analysis. In previous work, input and output Self-Organizing Maps (SOMs) were associated for causal analysis of multivariate and nonlinear data. Based on the association, a weight distribution of the output conditional on a given input was obtained over the output map space. Such a weighted SOM pattern of the output changes when the input changes. In order to analyze the change, it is important to measure the difference of the patterns. Many methods have been proposed for the dissimilarity measure of patterns. However, it remains a major challenge when attempting to measure how the patterns change. In this paper, we propose a visualization approach that simplifies the comparison of the difference in terms of the pattern property. Using this approach, the change can be analyzed by integrating colors and star glyph shapes representing the property dissimilarity. Ecological data is used to demonstrate the usefulness of our approach and the experimental results show that our approach provides the change information effectively.