Screening for major depressive disorder in a tertiary mental health centre using EarlyDetect: A machine learning-based pilot study

Liu, Yang S.; Hankey, Jeffrey; Cao, Bo; Chokka, Pratap

doi:10.1016/j.jadr.2020.100062

Cited by 18 publications

(23 citation statements)

References 48 publications

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“…In 2021, Liu et al [93] applied machine learning algorithms in a study on mental health centers. They found that ElasticNet yielded a more focused result and reduced the overfitting problem.…”

Section: 1rq1mentioning

confidence: 99%

“…T an et al (2018) [131] T hey applied Computer Adaptive T esting (CAT ) on the 1,135 participants taken as samples for T his study divides the sample data into two formats: one is for construction and the other is for simulation purposes. T hey did CAT depression Sensitivity: 93 [134] T hey used the PHQ-9 and applied machine learning algorithms. T hey used the autoencoder and further SVM algorithm was applied.…”

Section: Shah Et Al (2018) [130]mentioning

confidence: 99%

“…In total, they included the 300 participants in which 180 patients had the Bipolar, 49 had the MDD and 71 had the major depressive episodes. earlydetect questionnaire [93] The participants were asked to fill the earlydetect questionnaire. They performed this study on 955 participants from June 2016 to October 2018 which was approved by University of Alberta Research Board.…”

Section: Data Provided By Statistics Netherlands [91]mentioning

confidence: 99%

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A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection

Chahar¹,

Dubey²,

Narang³

2021

IJATEE

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Section: 1rq1mentioning

confidence: 99%

Section: Shah Et Al (2018) [130]mentioning

confidence: 99%

Section: Data Provided By Statistics Netherlands [91]mentioning

confidence: 99%

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A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection

Chahar¹,

Dubey²,

Narang³

2021

IJATEE

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“…Modeling based on machine learning (ML) is gaining traction in psychiatry 20 and has been successfully applied in mood disorder screening 21 – 24 . ML-based methods are also widely explored in the broader contexts of personalized medicine and healthcare utilizing EMR data 25 , 26 .…”

Section: Introductionmentioning

confidence: 99%

Individualized identification of sexual dysfunction of psychiatric patients with machine-learning

Liu

Hankey

Chokka

et al. 2022

Sci Rep

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Sexual dysfunction (SD) is prevalent in patients with mental health disorders and can significantly impair their quality of life. Early recognition of SD in a clinical setting may help patients and clinicians to optimize treatment options of SD and/or other primary diagnoses taking SD risk into account and may facilitate treatment compliance. SD identification is often overlooked in clinical practice; we seek to explore whether patients with a high risk of SD can be identified at the individual level by assessing known risk factors via a machine learning (ML) model. We assessed 135 subjects referred to a tertiary mental health clinic in a Western Canadian city using health records data, including age, sex, physician’s diagnoses, drug treatment, and the Arizona Sexual Experiences Scale (ASEX). A ML model was fitted to the data, with SD status derived from the ASEX as target outcomes and all other variables as predicting variables. Our ML model was able to identify individual SD cases—achieving a balanced accuracy of 0.736, with a sensitivity of 0.750 and a specificity of 0.721—and identified major depressive disorder and female sex as risk factors, and attention deficit hyperactivity disorder as a potential protective factor. This study highlights the utility of SD screening in a psychiatric clinical setting, demonstrating a proof-of-concept ML approach for SD screening in psychiatric patients, which has marked potential to improve their quality of life.

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“…Ciobanu et al [ 14 ] proposed a fuzzy forests machine learning model which was able to estimate recurrent MDD with an accuracy of 63% in an elderly population using transcriptome data. Liu et al [ 15 ] utilized an elastic net machine learning algorithm to predict MDD using self-reported questionnaires (AUC = 0.78). Finally, a recent study by Arloth et al [ 16 ] reported an integrated machine learning and genome-wide analysis approach to identify regulatory SNPs which are associated with MDD using expression quantitative trait loci (eQTLs) and methylation quantitative trait loci information.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach

Lin

Kuo

Lin

et al. 2021

JPM

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In light of recent advancements in machine learning, personalized medicine using predictive algorithms serves as an essential paradigmatic methodology. Our goal was to explore an integrated machine learning and genome-wide analysis approach which targets the prediction of probable major depressive disorder (MDD) using 9828 individuals in the Taiwan Biobank. In our analysis, we reported a genome-wide significant association with probable MDD that has not been previously identified: FBN1 on chromosome 15. Furthermore, we pinpointed 17 single nucleotide polymorphisms (SNPs) which show evidence of both associations with probable MDD and potential roles as expression quantitative trait loci (eQTLs). To predict the status of probable MDD, we established prediction models with random undersampling and synthetic minority oversampling using 17 eQTL SNPs and eight clinical variables. We utilized five state-of-the-art models: logistic ridge regression, support vector machine, C4.5 decision tree, LogitBoost, and random forests. Our data revealed that random forests had the highest performance (area under curve = 0.8905 ± 0.0088; repeated 10-fold cross-validation) among the predictive algorithms to infer complex correlations between biomarkers and probable MDD. Our study suggests that an integrated machine learning and genome-wide analysis approach may offer an advantageous method to establish bioinformatics tools for discriminating MDD patients from healthy controls.

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Screening for major depressive disorder in a tertiary mental health centre using EarlyDetect: A machine learning-based pilot study

Cited by 18 publications

References 48 publications

A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection

A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection

Individualized identification of sexual dysfunction of psychiatric patients with machine-learning

Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach

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