ArticleWorld journal of surgical oncology2026
Modeling distress in cancer patients using a fuzzy logic approach based on expert consensus.
Article in World journal of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo develop and validate a fuzzy logic model based on expert consensus to elucidate distress dynamics in cancer patients, examining the non-linear interactions between psychological, social, and medical factors.
methodsA two-round Delphi process with 23 psychosocial oncology experts was conducted to generate an interaction matrix of 18 distress-related variables. Using the skfuzzy Python library, a Mamdani fuzzy inference system was constructed, focusing on Negative Psychological Factors, Symptoms, and Positive Psychological Factors as primary drivers. Model validation included network analysis, time-series simulations, and sensitivity analyses, compared against a traditional crisp system dynamics model.
resultsThe fuzzy model confirmed a self-reinforcing "vicious cycle" of distress driven by Negative Psychological Factors (weight = 2.00) and Symptoms (weight = 1.50). Simulations demonstrated that positive psychological interventions could reduce overall distress levels by up to 25%. Network analysis identified distress as a central system hub, while the fuzzy model produced smoother, more clinically realistic trajectories than the crisp model.
conclusionThis study provides a robust mathematical explanation for the success of the validated DIC-2 clinical tool. The results underscore the necessity of early, multidisciplinary interventions to disrupt distress cycles, supporting the clinical shift toward treating distress as the "sixth vital sign".
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