Evidence map›Paper›PMID 34574929›Full record

ArticleHealthcare (Basel, Switzerland)2021

Comorbidity Patterns of Mood Disorders in Adult Inpatients: Applying Association Rule Mining.

Sunkyung Cha, Sung-Soo Kim

Open access · goldAbstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 34% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 7 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 1 country.

Sunkyung ChaDepartment of Nursing Science, Sunmoon University, Asan 31460, Korea.ORCID 0000-0003-4856-0608
Sung-Soo KimDepartment of Health Administration & Healthcare, Cheongju University, Cheongju 28503, Korea.ORCID 0000-0002-7205-0758
Cheongju University · KRSun Moon University · KR

Funding

Sun Moon University 2021
6 · The paper itself

Abstract

This study explored physical and psychiatric comorbidities of mood disorders using association rule mining. There were 7709 subjects who were patients (≥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.

Indexed as

ARM (association rule mining)comorbidityKorean national hospital discharge in-depth injury survey (KNHDS)mood disorder

Identifiers

PMID34574929
PMCPMC8470302
OpenAlexW3198701141

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.