On the criteria for diagnosing depression in bereaved individuals: a self-organizing map approach

R. Loula; L. H. A. Monteiro; R. Loula; L. H. A. Monteiro

doi:10.3934/mbe.2022252

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 6: 5380-5392. doi: 10.3934/mbe.2022252

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On the criteria for diagnosing depression in bereaved individuals: a self-organizing map approach

R. Loula ¹,
L. H. A. Monteiro ^{1,2
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1.
Universidade Presbiteriana Mackenzie, PPGEEC, São Paulo, SP, Brazil
2.
Universidade de São Paulo, Escola Politécnica, São Paulo, SP, Brazil

Academic Editor: Ivan V. Izonin

Received: 16 January 2022 Revised: 03 March 2022 Accepted: 11 March 2022 Published: 25 March 2022

Bereavement exclusion (BE) is a criterion for excluding the diagnosis of major depressive disorder (MDD). Simplistically, this criterion states that an individual who reports MDD symptoms should not be diagnosed as suffering from this mental illness, if such an individual is grieving a sorrowful loss. BE was introduced in 1980 to avoid confusing MDD with normal grief, because several cognitive and physical symptoms of grief and depression can look similar. However, in 2013, BE was removed from the MDD diagnosis guidelines. Here, this controversial topic is computationally investigated. A virtual population is generated according to the Brazilian data of death rate and MDD prevalence and its five kinds of individuals are clustered by using a Kohonen's self-organizing map (SOM). In addition, by examining the current guidelines for diagnosing MDD from an analytical perspective, a slight modification is proposed. With this modification, an adequate clustering is achieved by the SOM neural network. Therefore, for mathematical consistency, unbalanced scores should be assigned to the items composing the MDD diagnostic criteria. With the proposed criteria, the co-occurrence of normal grief and MDD can also be satisfactorily clustered.
- bereavement exclusion,
- grief,
- Kohonen network,
- major depressive disorder,
- psychometrics,
- self-organizing map
Citation: R. Loula, L. H. A. Monteiro. On the criteria for diagnosing depression in bereaved individuals: a self-organizing map approach[J]. Mathematical Biosciences and Engineering, 2022, 19(6): 5380-5392. doi: 10.3934/mbe.2022252

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Abstract

Bereavement exclusion (BE) is a criterion for excluding the diagnosis of major depressive disorder (MDD). Simplistically, this criterion states that an individual who reports MDD symptoms should not be diagnosed as suffering from this mental illness, if such an individual is grieving a sorrowful loss. BE was introduced in 1980 to avoid confusing MDD with normal grief, because several cognitive and physical symptoms of grief and depression can look similar. However, in 2013, BE was removed from the MDD diagnosis guidelines. Here, this controversial topic is computationally investigated. A virtual population is generated according to the Brazilian data of death rate and MDD prevalence and its five kinds of individuals are clustered by using a Kohonen's self-organizing map (SOM). In addition, by examining the current guidelines for diagnosing MDD from an analytical perspective, a slight modification is proposed. With this modification, an adequate clustering is achieved by the SOM neural network. Therefore, for mathematical consistency, unbalanced scores should be assigned to the items composing the MDD diagnostic criteria. With the proposed criteria, the co-occurrence of normal grief and MDD can also be satisfactorily clustered.

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