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The concept of individual semantic maps in clinical psychology: a feasibility study on a new paradigm

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  • Enzo Grossi
  • Angelo Compare
  • Massimo Buscema

Abstract

In this paper we propose a new technology able to map the underlying connection scheme among several psychological variables in a single individual. Nine patients with chronic heart failure underwent at regular intervals, two electronic questionnaires to evaluate depression (STAI—short form) and anxiety (STAY-6). Individual semantic maps were developed by Auto Contractive Map, a new data mining tool based on an artificial neural networks acting on the small data set formed by questionnaires items applied serially along time. The clinical psychologist involved in the clinical evaluation of the cases was asked to score the consistency between the information emerging from the graph depicting the structure of the main relationships among items and the clinical picture resulting from the psychological colloquium. All cases reported overall judgments of a good consistency suggesting that the mathematical architecture of the system is able to capture in the dynamics of items value variations through time the underlying construct of the patient psychological status. This technology is promising in remote monitoring of patients’ psychological condition in different settings with the possibility to implement personalized psychological interventions. Copyright Springer Science+Business Media B.V. 2014

Suggested Citation

  • Enzo Grossi & Angelo Compare & Massimo Buscema, 2014. "The concept of individual semantic maps in clinical psychology: a feasibility study on a new paradigm," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(1), pages 15-35, January.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:1:p:15-35
    DOI: 10.1007/s11135-012-9746-8
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    Cited by:

    1. Buscema, Massimo & Sacco, Pier Luigi, 2016. "MST Fitness Index and implicit data narratives: A comparative test on alternative unsupervised algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 726-746.
    2. Nida Shahid & Tim Rappon & Whitney Berta, 2019. "Applications of artificial neural networks in health care organizational decision-making: A scoping review," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-22, February.
    3. Shuting Chen & Dapeng Tan, 2018. "A SA-ANN-Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm," Complexity, Hindawi, vol. 2018, pages 1-21, January.

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