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

Abstract

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.

Fuzzy Cognitive Maps for Algorithmic Decision Making in eMental Health

“…FCMs are commonly used to represent knowledge about a specific dynamic system, including knowledge systems in the area of decision support, time series forecasting and pattern classification [20], and to model the development of the system over time using either experts' experiences, historical data, or both. In recent years, FCMs have been applied in clinical decision-making [22], including the mental health domain [23,15,14,24]. For example, Farahani et al [14] have developed a FCM to investigate the causal relationships between factors that influence psychological well-being in university students, including social support, resilience, and personal values such as hedonism and benevolence.…”