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Network Theory and Switching Behaviors: A User Guide for Analyzing Electronic Records Databases

Author

Listed:
  • Giorgio Gronchi

    (Section of Psychology, Department of Neuroscience, Psychology, Drug Research and Child’s Health (NEUROFARBA), University of Florence, 12 Via di San Salvi, 50135 Firenze, Italy)

  • Marco Raglianti

    (Software Institute, Università della Svizzera Italiana (USI), 1 Via la Santa, 6962 Lugano, Switzerland)

  • Fabio Giovannelli

    (Section of Psychology, Department of Neuroscience, Psychology, Drug Research and Child’s Health (NEUROFARBA), University of Florence, 12 Via di San Salvi, 50135 Firenze, Italy)

Abstract

As part of studies that employ health electronic records databases, this paper advocates the employment of graph theory for investigating drug-switching behaviors. Unlike the shared approach in this field (comparing groups that have switched with control groups), network theory can provide information about actual switching behavior patterns. After a brief and simple introduction to fundamental concepts of network theory, here we present (i) a Python script to obtain an adjacency matrix from a records database and (ii) an illustrative example of the application of network theory basic concepts to investigate drug-switching behaviors. Further potentialities of network theory (weighted matrices and the use of clustering algorithms), along with the generalization of these methods to other kinds of switching behaviors beyond drug switching, are discussed.

Suggested Citation

  • Giorgio Gronchi & Marco Raglianti & Fabio Giovannelli, 2021. "Network Theory and Switching Behaviors: A User Guide for Analyzing Electronic Records Databases," Future Internet, MDPI, vol. 13(9), pages 1-12, August.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:9:p:228-:d:626487
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    References listed on IDEAS

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