Cargando…
Comorbidity Patterns of Mood Disorders in Adult Inpatients: Applying Association Rule Mining
This study explored physical and psychiatric comorbidities of mood disorders using association rule mining. There were 7709 subjects who were patients ([Formula: see text] 19 years old) diagnosed with mood disorders and included in the data collected by the Korean National Hospital Discharge In-dept...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8470302/ https://www.ncbi.nlm.nih.gov/pubmed/34574929 http://dx.doi.org/10.3390/healthcare9091155 |
_version_ | 1784574164170964992 |
---|---|
author | Cha, Sunkyung Kim, Sung-Soo |
author_facet | Cha, Sunkyung Kim, Sung-Soo |
author_sort | Cha, Sunkyung |
collection | PubMed |
description | This study explored physical and psychiatric comorbidities of mood disorders using association rule mining. There were 7709 subjects who were patients ([Formula: see text] 19 years old) diagnosed with mood disorders and included in the data collected by the Korean National Hospital Discharge In-depth Injury Survey (KNHDS) between 2006 and 2018. Physical comorbidities (46.17%) were higher than that of psychiatric comorbidities (27.28%). The frequent comorbidities of mood disorders (F30–F39) were hypertensive diseases (I10–I15), neurotic, stress-related and somatoform disorders (F40–F48), diabetes mellitus (E10–E14), and diseases of esophagus, stomach, and duodenum (K20–K31). The bidirectional association path of mood disorders (F30–F39) with hypertensive diseases (I10–I15) and diabetes mellitus (E10–E14) were the strongest. Depressive episodes (F32) and recurrent depressive disorders (F33) revealed strong bidirectional association paths with other degenerative diseases of the nervous system (G30-G32) and organic, including symptomatic and mental disorders (F00–F09). Bipolar affective disorders (F31) revealed strong bidirectional association paths with diabetes mellitus (E10–E14) and hypertensive diseases (I10–I15). It was found that different physical and psychiatric disorders are comorbid according to the sub-classification of mood disorders. Understanding the comorbidity patterns of major comorbidities for each mood disorder can assist mental health providers in treating and managing patients with mood disorders. |
format | Online Article Text |
id | pubmed-8470302 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84703022021-09-27 Comorbidity Patterns of Mood Disorders in Adult Inpatients: Applying Association Rule Mining Cha, Sunkyung Kim, Sung-Soo Healthcare (Basel) Article This study explored physical and psychiatric comorbidities of mood disorders using association rule mining. There were 7709 subjects who were patients ([Formula: see text] 19 years old) diagnosed with mood disorders and included in the data collected by the Korean National Hospital Discharge In-depth Injury Survey (KNHDS) between 2006 and 2018. Physical comorbidities (46.17%) were higher than that of psychiatric comorbidities (27.28%). The frequent comorbidities of mood disorders (F30–F39) were hypertensive diseases (I10–I15), neurotic, stress-related and somatoform disorders (F40–F48), diabetes mellitus (E10–E14), and diseases of esophagus, stomach, and duodenum (K20–K31). The bidirectional association path of mood disorders (F30–F39) with hypertensive diseases (I10–I15) and diabetes mellitus (E10–E14) were the strongest. Depressive episodes (F32) and recurrent depressive disorders (F33) revealed strong bidirectional association paths with other degenerative diseases of the nervous system (G30-G32) and organic, including symptomatic and mental disorders (F00–F09). Bipolar affective disorders (F31) revealed strong bidirectional association paths with diabetes mellitus (E10–E14) and hypertensive diseases (I10–I15). It was found that different physical and psychiatric disorders are comorbid according to the sub-classification of mood disorders. Understanding the comorbidity patterns of major comorbidities for each mood disorder can assist mental health providers in treating and managing patients with mood disorders. MDPI 2021-09-03 /pmc/articles/PMC8470302/ /pubmed/34574929 http://dx.doi.org/10.3390/healthcare9091155 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cha, Sunkyung Kim, Sung-Soo Comorbidity Patterns of Mood Disorders in Adult Inpatients: Applying Association Rule Mining |
title | Comorbidity Patterns of Mood Disorders in Adult Inpatients: Applying Association Rule Mining |
title_full | Comorbidity Patterns of Mood Disorders in Adult Inpatients: Applying Association Rule Mining |
title_fullStr | Comorbidity Patterns of Mood Disorders in Adult Inpatients: Applying Association Rule Mining |
title_full_unstemmed | Comorbidity Patterns of Mood Disorders in Adult Inpatients: Applying Association Rule Mining |
title_short | Comorbidity Patterns of Mood Disorders in Adult Inpatients: Applying Association Rule Mining |
title_sort | comorbidity patterns of mood disorders in adult inpatients: applying association rule mining |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8470302/ https://www.ncbi.nlm.nih.gov/pubmed/34574929 http://dx.doi.org/10.3390/healthcare9091155 |
work_keys_str_mv | AT chasunkyung comorbiditypatternsofmooddisordersinadultinpatientsapplyingassociationrulemining AT kimsungsoo comorbiditypatternsofmooddisordersinadultinpatientsapplyingassociationrulemining |