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...

Descripción completa

Detalles Bibliográficos
Autores principales: Cha, Sunkyung, Kim, Sung-Soo
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