A Dynamic Probabilistic Model for Heterogeneous Data Fusion: A Pilot Case Study from Computer-Aided Detection of Depression

Author:

Vitale Federica1ORCID,Carbonaro Bruno1,Esposito Anna23ORCID

Affiliation:

1. Department of Mathematics and Physics, University of Campania “L. Vanvitelli”, Viale Lincoln 5, 81100 Caserta, Italy

2. Department of Psychology, Università degli Studi della Campania “L. Vanvitelli”, Viale Ellittico 31, 81100 Caserta, Italy

3. International Institute for Advanced Scientific Studies (IIASS), 84019 Vietri sul Mare, Italy

Abstract

The present paper, in the framework of a search for a computer-aided method to detect depression, deals with experimental data of various types, with their correlation, and with the way relevant information about depression delivered by different sets of data can be fused to build a unique body of knowledge about individuals’ mental states facilitating the diagnosis and its accuracy. To this aim, it suggests the use of a recently introduced «limiting form» of the kinetic-theoretic language, at present widely used to describe complex systems of objects of the most diverse nature. In this connection, the paper mainly aims to show how a wide range of experimental procedures can be described as examples of this «limiting case» and possibly rendered by this description more effective as methods of prediction from experience. In particular, the paper contains a simple, preliminary application of the method to the detection of depression, to show how the consideration of statistical parameters connected with the analysis of speech can modify, at least in a stochastic sense, each diagnosis of depression delivered by the Beck Depression Inventory (BDI-II).

Funder

Università della Campania “Luigi Vanvitelli” V:ALERE 2019

Publisher

MDPI AG

Subject

General Neuroscience

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1. Some remarks on vector Markov Chains and their applications to the description of many-particle systems;Stochastic Processes - Theoretical Advances and Applications in Complex Systems;2024-07-31

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