Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study

Abstract

Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their ability to model complex relationships between symptoms, biomarkers, risk factors, and treatments has enabled healthcare providers to make informed decisions, leading to better patient outcomes. This review article provides a thorough synopsis of using FCMs within the medical domain. A systematic examination of pertinent literature spanning the last two decades forms the basis of this overview, specifically delineating the diverse applications of FCMs in medical realms, including decision-making, diagnosis, prognosis, treatment optimisation, risk assessment, and pharmacovigilance. The limitations inherent in FCMs are also scrutinised, and avenues for potential future research and application are explored.

Fuzzy cognitive mapping in participatory research and decision making: a practice review

“…Our experiences with FCM resonate with and adds considerable detail to earlier FCM authors [ 18 , 19 ], including those offering protocols for meaningful participation in environmental sciences [ 16 , 49 ]. The most recent literature reviews on the use of FCM have not discussed the contributions we described here [ 20 , 22 , 55 , 56 ] and do not provide details on practical decisions across the mapping process or on the implications of stakeholder authorship. This review provides practical insights for FCM researchers before they generate maps, during data collection and in analysis.…”

“…The flexibility of the technique allows for systematic mapping of knowledge from multiple sources to identify influences on a particular outcome while supporting collective learning and decision making [ 16 ]. FCM has been used across multiple fields with applications that include modelling, prediction, monitoring, decision-making, and management [ 17 – 20 ]. FCM has been applied in medicine to aid diagnosis and treatment decision-making [ 21 , 22 ].…”

Investigating Genetic and Familial Risks in Childhood ALL: A Longitudinal Virtual Study Using aiHumanoid Simulations of TEL-AML1 Gene Fusion

“…FCMs utilize fuzzy or grey scale logic to map causal relationships and their strength between various components of a system, such as genetic factors, environmental influences, and cellular pathways. This modeling technique which most resembles fully connected, recurrent artificial neural networks (RNN) has been successfully applied in various fields of medicine and is particularly suited for capturing the dynamic and multifactorial nature of diseases like ALL [7,8,9,10].…”