Bayesian modeling of clinical psychologists’ diagnostic decision-making processes under conditions of limited information

Abstract

The process of making diagnostic decisions in clinical psychology is characterized by a high level of uncertainty due to the limited, incomplete, and heterogeneous nature of psychodiagnostic information. In such conditions, the use of formalized probabilistic approaches capable of ensuring consistent refinement of diagnostic assessments becomes particularly important. The purpose of the article is to develop and scientifically substantiate a Bayesian approach to modeling the process of diagnostic decision-making by clinical psychologists, which provides formalized refinement of the probabilities of alternative diagnostic hypotheses and increases the validity of clinical conclusions in conditions of limited and incomplete psychodiagnostic information. The study uses a systematic and structural-functional analysis of the process of clinical and psychological diagnosis, logical-probabilistic modeling, and the method of sequential Bayesian updating of diagnostic assessments. Applied modeling of the process of refining diagnostic hypotheses was carried out based on the results of clinical interviews and standardized psychodiagnostic techniques using the mechanism of a posteriori assessment of their probability. As a result of the study, the specifics of the formation of diagnostic decisions in conditions of information uncertainty were clarified, and it was established that the process of clinical conclusion is dynamic and probabilistic in nature. The possibility of applying the Bayesian approach to formalize the process of integrating psychodiagnostic data was substantiated. The modeling demonstrated that consistent consideration of the results of psychodiagnostic instruments provides clarification of the probability of alternative diagnostic hypotheses and reduces the level of diagnostic uncertainty. It was found that standardized psychometric indicators, which allow for quantitative justification of the diagnostic decision, have the greatest diagnostic informativeness. The conclusions indicate that the use of the Bayesian approach provides a formalized assessment of the validity of diagnostic hypotheses and increases the accuracy, objectivity, and reproducibility of clinical decisions. Limitations of the approach have been identified, related to insufficient empirical data, the complexity of formalizing clinical signs, and the variability of individual manifestations of mental states. The feasibility of using Bayesian models as a tool to support diagnostic decision-making has been substantiated