Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning.
Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning.
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DOI：
10.1186/s12961-018-0308-y
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发表时间：
2018-04-25
影响因子：
4
通讯作者：
García-Alonso CR
中科院分区：
文献类型：
--
作者：
Chung Y;Salvador-Carulla L;Salinas-Pérez JA;Uriarte-Uriarte JJ;Iruin-Sanz A;García-Alonso CR
精神卫生系统的决策应得到数据的循证知识转移的支持。由于心理健康系统本身就很复杂，涉及其结构，过程和结果之间的相互作用，决策支持系统（DSS）需要使用先进的计算方法和可视化工具来开发，以允许完整的系统分析，同时将领域专家纳入分析过程。在这项研究中，我们使用的DSS模型开发的交互式数据挖掘和领域专家合作的分析复杂的精神卫生系统，以提高系统的知识和证据知情的政策规划。我们结合联合收割机的交互式可视化数据挖掘方法，自组织地图网络（SOMNet），与操作的专家知识的方法，基于专家的协作分析（EbCA），开发一个DSS模型。SOMNet被应用于西班牙三个不同区域精神卫生系统的医疗模式和指标分析，包括106个小集水区，为900多万居民提供医疗保健。基于EbCA，开发团队中的领域专家指导并评估了分析过程和结果。另一组由13名心理健康系统规划和研究领域专家组成的小组根据SOMNet方法的分析信息评估了该模型，以在现实世界中处理信息和发现知识。通过评估，领域专家评估了DSS模型的可行性和技术准备水平（TRL）。SOMNet与EbCA相结合，在分析系统异常值、解释全球和地方模式以及通过分析解释完善关键绩效指标时，有效地处理了循证信息。领域专家对该模型的评价结果表明，该模型是可行的，达到了TRL（业务环境中的系统原型演示）的7级。这项研究支持卫生系统工程（SOMNet）和专家知识（EbCA）相结合，分析卫生系统研究的复杂性的好处。SOMNet方法的使用有助于在实践中的心理健康规划的DSS的示范。本文的在线版本（10.1186/s12961-018-0308-y）包含补充材料，可供授权用户使用。
Decision-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. We 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. The 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). This 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. The online version of this article (10.1186/s12961-018-0308-y) contains supplementary material, which is available to authorized users.
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影响因子：
4
作者：
Gibert K;García-Alonso C;Salvador-Carulla L
通讯作者：
Salvador-Carulla L
影响因子：
4.5
作者：
Carroll LN;Au AP;Detwiler LT;Fu TC;Painter IS;Abernethy NF
通讯作者：
Abernethy NF
影响因子：
1.8
作者：
Fernandez, Ana;Gillespie, James A.;Salvador-Carulla, Luis
通讯作者：
Salvador-Carulla, Luis
影响因子：
120.7
作者：
Berwick, Donald M.;Hackbarth, Andrew D.
通讯作者：
Hackbarth, Andrew D.
DOI：
10.1007/978-3-540-71080-6_6
发表时间：
2008-01-01
期刊：
VISUAL DATA MINING: THEORY, TECHNIQUES AND TOOLS FOR VISUAL ANALYTICS
影响因子：
--
作者：
Keim, Daniel A.;Mansmann, Florian;Ziegler, Hartmut
通讯作者：
Ziegler, Hartmut