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

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 k...

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Bibliographic Details
Published in:Brain sciences 2023-09, Vol.13 (9), p.1339
Main Authors: Vitale, Federica, Carbonaro, Bruno, Esposito, Anna
Format: Article
Language:English
Subjects:
Beck, Aaron T
Behavior
Diagnosis
Experiments
Handwriting
kinetic theory
Mathematical models
Mental depression
Operating systems
probability
Random variables
Reading
Software
Speech
speech recognition
statistical parametric speech synthesis
Stochasticity
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Summary: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).
ISSN:2076-3425
2076-3425
DOI:10.3390/brainsci13091339