An exploratory statistical approach to depression pattern identification
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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 392 (2013)
Issue (Month): 4 ()
|Contact details of provider:|| Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Pilgrim, David, 2007. "The survival of psychiatric diagnosis," Social Science & Medicine, Elsevier, vol. 65(3), pages 536-547, August.
- 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.
When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:392:y:2013:i:4:p:889-901. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Shamier, Wendy)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.