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

ABSTRACT: BACKGROUND: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. METHODS: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. RESULTS: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). CONCLUSIONS: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.

SUBMITTER: Chung Y

PROVIDER: S-EPMC5922302 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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

Chung Younjin Y Salvador-Carulla Luis L Salinas-Pérez José A JA Uriarte-Uriarte Jose J JJ Iruin-Sanz Alvaro A García-Alonso Carlos R CR

Health research policy and systems 20180425 1

<h4>Background</h4>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 fo ...[more]

PMID: 29695248

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Project description:Endotherms such as rats and mice huddle together to keep warm. The huddle is considered to be an example of a self-organising system, because complex properties of the collective group behaviour are thought to emerge spontaneously through simple interactions between individuals. Groups of rodent pups display two such emergent properties. First, huddling undergoes a 'phase transition', such that pups start to aggregate rapidly as the temperature of the environment falls below a critical temperature. Second, the huddle maintains a constant 'pup flow', where cooler pups at the periphery continually displace warmer pups at the centre. We set out to test whether these complex group behaviours can emerge spontaneously from local interactions between individuals. We designed a model using a minimal set of assumptions about how individual pups interact, by simply turning towards heat sources, and show in computer simulations that the model reproduces the first emergent property--the phase transition. However, this minimal model tends to produce an unnatural behaviour where several smaller aggregates emerge rather than one large huddle. We found that an extension of the minimal model to include heat exchange between pups allows the group to maintain one large huddle but eradicates the phase transition, whereas inclusion of an additional homeostatic term recovers the phase transition for large huddles. As an unanticipated consequence, the extended model also naturally gave rise to the second observed emergent property--a continuous pup flow. The model therefore serves as a minimal description of huddling as a self-organising system, and as an existence proof that group-level huddling dynamics emerge spontaneously through simple interactions between individuals. We derive a specific testable prediction: Increasing the capacity of the individual to generate or conserve heat will increase the range of ambient temperatures over which adaptive thermoregulatory huddling will emerge.

Project description:BackgroundA clinical algorithm (Algo) in paper form is used in Quebec, Canada, to allow health care workers other than occupational therapists (OTs) to make bathroom adaptation recommendations for older adults. An integrated knowledge transfer process around Algo suggested an electronic version of this decision support system (electronic decision support system [e-DSS]) to be used by older adults and their caregivers in search of information and solutions for their autonomy and safety in the bathroom.ObjectiveThis study aims to (1) create an e-DSS for the self-selection of bathroom-assistive technology by community-dwelling older adults and their caregivers and (2) assess usability with lay users and experts to improve the design accordingly.MethodsOn the basis of a user-centered design approach, the process started with content identification for the prototype through 7 semistructured interviews with key informants of various backgrounds (health care providers, assistive technology providers, and community services) and 4 focus groups (2 with older adults and 2 with caregivers). A thematic content transcript analysis was carried out and used during the creation of the prototype. The prototype was refined iteratively using think-aloud and observation methods with a clinical expert (n=1), researchers (n=3), OTs (n=3), older adults (n=3), and caregivers (n=3), who provided information on the usability of the e-DSS.ResultsOverall, 4 themes served as the criteria for the prototype of the electronic Algo (Hygiene 2.0 [H2.0]): focus (safety, confidentiality, well-being, and autonomy), engage, facilitate (simplify, clarify, and illustrate), and access. For example, users first pay attention to the images (engage and illustrate) that can be used to depict safe postures (safety), illustrate questions embedded in the decision support tool (clarify and illustrate), and demonstrate the context of the use of assistive technology (safety and clarify).ConclusionsThe user-centered design of H2.0 allowed the cocreation of an e-DSS in the form of a website, in line with the needs of community-dwelling older adults and their caregivers seeking bathroom-assistive technology that enables personal hygiene. Each iteration improved usability and brought more insight into the users' realities, tailoring the e-DSS to the implementation context.

Project description:A protein's structure is the key to its function. As protein structure can vary with environment, it is important to be able to determine it over a wide range of concentrations, temperatures, formulation vehicles, and states. Robust reproducible validated methods are required for applications including batch-batch comparisons of biopharmaceutical products. Circular dichroism is widely used for this purpose, but an alternative is required for concentrations above 10 mg/mL or for solutions with chiral buffer components that absorb far UV light. Infrared (IR) protein absorbance spectra of the Amide I region (1,600-1700 cm-1) contain information about secondary structure and require higher concentrations than circular dichroism often with complementary spectral windows. In this paper, we consider a number of approaches to extract structural information from a protein infrared spectrum and determine their reliability for regulatory and research purpose. In particular, we compare direct and second derivative band-fitting with a self-organising map (SOM) approach applied to a number of different reference sets. The self-organising map (SOM) approach proved significantly more accurate than the band-fitting approaches for solution spectra. As there is no validated benchmark method available for infrared structure fitting, SOMSpec was implemented in a leave-one-out validation (LOOV) approach for solid-state transmission and thin-film attenuated total reflectance (ATR) reference sets. We then tested SOMSpec and the thin-film ATR reference set against 68 solution spectra and found the average prediction error for helix (α + 310) and β-sheet was less than 6% for proteins with less than 40% helix. This is quantitatively better than other available approaches. The visual output format of SOMSpec aids identification of poor predictions. We also demonstrated how to convert aqueous ATR spectra to and from transmission spectra for structure fitting. Fourier self-deconvolution did not improve the average structure predictions.

Dataset Information

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

Publications

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

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