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An exploratory statistical approach to depression pattern identification

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Author Info

  • Feng, Qing Yi
  • Griffiths, Frances
  • Parsons, Nick
  • Gunn, Jane
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    Abstract

    Depression is a complex phenomenon thought to be due to the interaction of biological, psychological and social factors. Currently depression assessment uses self-reported depressive symptoms but this is limited in the degree to which it can characterise the different expressions of depression emerging from the complex causal pathways that are thought to underlie depression. In this study, we aimed to represent the different patterns of depression with pattern values unique to each individual, where each value combines all the available information about an individual’s depression. We considered the depressed individual as a subsystem of an open complex system, proposed Generalized Information Entropy (GIE) to represent the general characteristics of information entropy of the system, and then implemented Maximum Entropy Estimates to derive equations for depression patterns. We also introduced a numerical simulation method to process the depression related data obtained by the Diamond Cohort Study which has been underway in Australia since 2005 involving 789 people. Unlike traditional assessment, we obtained a unique value for each depressed individual which gives an overall assessment of the depression pattern. Our work provides a novel way to visualise and quantitatively measure the depression pattern of the depressed individual which could be used for pattern categorisation. This may have potential for tailoring health interventions to depressed individuals to maximize health benefit.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0378437112009211
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    Bibliographic Info

    Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

    Volume (Year): 392 (2013)
    Issue (Month): 4 ()
    Pages: 889-901

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    Handle: RePEc:eee:phsmap:v:392:y:2013:i:4:p:889-901

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    Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

    Related research

    Keywords: Depression; Pattern identification; Generalized Information Entropy; Maximum entropy estimates; Quantitative analysis; Visual representation;

    References

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    1. Chen, Li Ming & Chai, Li He, 2006. "A theoretical analysis on self-organized formation of microbial biofilms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 793-807.
    2. Feng, Qing Yi & Chai, Li He, 2008. "A new statistical dynamic analysis on vegetation patterns in land ecosystems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3583-3593.
    3. Pilgrim, David, 2007. "The survival of psychiatric diagnosis," Social Science & Medicine, Elsevier, vol. 65(3), pages 536-547, August.
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