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Developing an eMental health monitoring module for older mourners using fuzzy cognitive maps

OBJECTIVE: Effective internet interventions often combine online self-help with regular professional guidance. In the absence of regularly scheduled contact with a professional, the internet intervention should refer users to professional human care if their condition deteriorates. The current artic...

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Autores principales: Brandl, Lena, van Velsen, Lex, Brodbeck, Jeannette, Jacinto, Sofia, Hofs, Dennis, Heylen, Dirk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10286164/
https://www.ncbi.nlm.nih.gov/pubmed/37361430
http://dx.doi.org/10.1177/20552076231183549
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author Brandl, Lena
van Velsen, Lex
Brodbeck, Jeannette
Jacinto, Sofia
Hofs, Dennis
Heylen, Dirk
author_facet Brandl, Lena
van Velsen, Lex
Brodbeck, Jeannette
Jacinto, Sofia
Hofs, Dennis
Heylen, Dirk
author_sort Brandl, Lena
collection PubMed
description OBJECTIVE: Effective internet interventions often combine online self-help with regular professional guidance. In the absence of regularly scheduled contact with a professional, the internet intervention should refer users to professional human care if their condition deteriorates. The current article presents a monitoring module to recommend proactively seeking offline support in an eMental health service to aid older mourners. METHOD: The module consists of two components: a user profile that collects relevant information about the user from the application, enabling the second component, a fuzzy cognitive map (FCM) decision-making algorithm that detects risk situations and to recommend the user to seek offline support, whenever advisable. In this article, we show how we configured the FCM with the help of eight clinical psychologists and we investigate the utility of the resulting decision tool using four fictitious scenarios. RESULTS: The current FCM algorithm succeeds in detecting unambiguous risk situations, as well as unambiguously safe situations, but it has more difficulty classifying borderline cases correctly. Based on recommendations from the participants and an analysis of the algorithm's erroneous classifications, we propose how the current FCM algorithm can be further improved. CONCLUSION: The configuration of FCMs does not necessarily demand large amounts of privacy-sensitive data and their decisions are scrutable. Thus, they hold great potential for automatic decision-making algorithms in mental eHealth. Nevertheless, we conclude that there is a need for clear guidelines and best practices for developing FCMs, specifically for eMental health.
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spelling pubmed-102861642023-06-23 Developing an eMental health monitoring module for older mourners using fuzzy cognitive maps Brandl, Lena van Velsen, Lex Brodbeck, Jeannette Jacinto, Sofia Hofs, Dennis Heylen, Dirk Digit Health Original Research OBJECTIVE: Effective internet interventions often combine online self-help with regular professional guidance. In the absence of regularly scheduled contact with a professional, the internet intervention should refer users to professional human care if their condition deteriorates. The current article presents a monitoring module to recommend proactively seeking offline support in an eMental health service to aid older mourners. METHOD: The module consists of two components: a user profile that collects relevant information about the user from the application, enabling the second component, a fuzzy cognitive map (FCM) decision-making algorithm that detects risk situations and to recommend the user to seek offline support, whenever advisable. In this article, we show how we configured the FCM with the help of eight clinical psychologists and we investigate the utility of the resulting decision tool using four fictitious scenarios. RESULTS: The current FCM algorithm succeeds in detecting unambiguous risk situations, as well as unambiguously safe situations, but it has more difficulty classifying borderline cases correctly. Based on recommendations from the participants and an analysis of the algorithm's erroneous classifications, we propose how the current FCM algorithm can be further improved. CONCLUSION: The configuration of FCMs does not necessarily demand large amounts of privacy-sensitive data and their decisions are scrutable. Thus, they hold great potential for automatic decision-making algorithms in mental eHealth. Nevertheless, we conclude that there is a need for clear guidelines and best practices for developing FCMs, specifically for eMental health. SAGE Publications 2023-06-19 /pmc/articles/PMC10286164/ /pubmed/37361430 http://dx.doi.org/10.1177/20552076231183549 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research
Brandl, Lena
van Velsen, Lex
Brodbeck, Jeannette
Jacinto, Sofia
Hofs, Dennis
Heylen, Dirk
Developing an eMental health monitoring module for older mourners using fuzzy cognitive maps
title Developing an eMental health monitoring module for older mourners using fuzzy cognitive maps
title_full Developing an eMental health monitoring module for older mourners using fuzzy cognitive maps
title_fullStr Developing an eMental health monitoring module for older mourners using fuzzy cognitive maps
title_full_unstemmed Developing an eMental health monitoring module for older mourners using fuzzy cognitive maps
title_short Developing an eMental health monitoring module for older mourners using fuzzy cognitive maps
title_sort developing an emental health monitoring module for older mourners using fuzzy cognitive maps
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10286164/
https://www.ncbi.nlm.nih.gov/pubmed/37361430
http://dx.doi.org/10.1177/20552076231183549
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