Application of machine learning in predicting the risk of postpartum depression: A systematic review

Zhong, Minhui; Zhang, Han; Yu, Chan; Jiang, Jinxia; Duan, Xia

doi:10.1016/j.jad.2022.08.070

Cited by 21 publications

(13 citation statements)

References 51 publications

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“…MVPA has also been used for distinguishing between different psychiatric disorders (Sundermann et al 2014), for example, between unipolar (major depressive disorder, MDD) and bipolar depression (Han et al 2019;Grotegerd et al 2014;Bürger et al 2017;Rubin-Falcone et al 2018). Moreover, it has also been applied to predict the risk of depression (Zhong et al 2022) and BD (Hajek et al 2015), as well as to identify clinical phenotypes of BD (Wu et al 2017), brain volume changes in BD (Sartori et al 2018), and to differentiate treatment response to electroconvulsive therapy in MDD (Redlich et al 2016). Taken together, these studies confirm the potential of machine leaning techniques in clinical settings, particularly to discover novel biomarkers.…”

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confidence: 62%

Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data

Kondo

Whitehead

Corbalán

et al. 2023

Int J Bipolar Disord

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Background Bipolar disorder type-I (BD-I) patients are known to show emotion regulation abnormalities. In a previous fMRI study using an explicit emotion regulation paradigm, we compared responses from 19 BD-I patients and 17 matched healthy controls (HC). A standard general linear model-based univariate analysis revealed that BD patients showed increased activations in inferior frontal gyrus when instructed to decrease their emotional response as elicited by neutral images. We implemented multivariate pattern recognition analyses on the same data to examine if we could classify conditions within-group as well as HC versus BD. Methods We reanalyzed explicit emotion regulation data using a multivariate pattern recognition approach, as implemented in PRONTO software. The original experimental paradigm consisted of a full 2 × 2 factorial design, with valence (Negative/Neutral) and instruction (Look/Decrease) as within subject factors. Results The multivariate models were able to accurately classify different task conditions when HC and BD were analyzed separately (63.24%–75.00%, p = 0.001–0.012). In addition, the models were able to correctly classify HC versus BD with significant accuracy in conditions where subjects were instructed to downregulate their felt emotion (59.60%–60.84%, p = 0.014–0.018). The results for HC versus BD classification demonstrated contributions from the salience network, several occipital and frontal regions, inferior parietal lobes, as well as other cortical regions, to achieve above-chance classifications. Conclusions Our multivariate analysis successfully reproduced some of the main results obtained in the previous univariate analysis, confirming that these findings are not dependent on the analysis approach. In particular, both types of analyses suggest that there is a significant difference of neural patterns between conditions within each subject group. The multivariate approach also revealed that reappraisal conditions provide the most informative activity for differentiating HC versus BD, irrespective of emotional valence (negative or neutral). The current results illustrate the importance of investigating the cognitive control of emotion in BD. We also propose a set of candidate regions for further study of emotional control in BD.

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confidence: 62%

Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data

Kondo

Whitehead

Corbalán

et al. 2023

Int J Bipolar Disord

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“…Support Vector Machine (SVM) is one of the many machine learning methods used for classification, regression and outlier detection and is a powerful method for recognizing subtle patterns in complex datasets. In recent years, the SVM technique has been widely used in medicine and ophthalmology [20,21]. In this study, we developed a multidimensional SLE depression risk prediction model using SVM.…”

Section: Discussionmentioning

confidence: 99%

SVM-Based Model Combining Patients’ Reported Outcomes and Lymphocyte Phenotypes of Depression in Systemic Lupus Erythematosus

et al. 2023

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Background: The incidence of depression in patients with systemic lupus erythematosus (SLE) is high and leads to a lower quality of life than that in undepressed SLE patients and healthy individuals. The causes of SLE depression are still unclear. Methods: A total of 94 SLE patients were involved in this study. A series of questionnaires (Hospital Depression Scale, Social Support Rate Scale and so on) were applied. Flow cytometry was used to test the different stages and types of T cells and B cells in peripheral blood mononuclear cells. Univariate and multivariate analyses were conducted to explore the key contributors to depression in SLE. Support Vector Machine (SVM) learning was applied to form the prediction model. Results: Depressed SLE patients showed lower objective support, severer fatigue, worse sleep quality and higher percentages of ASC%PBMC, ASC%CD19+, MAIT, TEM%Th, TEMRA%Th, CD45RA+CD27-Th, TEMRA%CD8 than non-depressed patients. A learning-based SVM model combining objective and patient-reported variables showed that fatigue, objective support, ASC%CD19+, TEM%Th and TEMRA%CD8 were the main contributing factors to depression in SLE. With the SVM model, the weight of TEM%Th was 0.17, which is the highest among objective variables, and the weight of fatigue was 0.137, which was the highest among variables of patients’ reported outcomes. Conclusions: Both patient-reported factors and immunological factors could be involved in the occurrence and development of depression in SLE. Scientists can explore the mechanism of depression in SLE or other psychological diseases from the above perspective.

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“… Qualitative nature limits generalizability. 21 Natural Language Processing Online Social Media Sleepiness detected by smart devices relates to PPD risk. Work only on textual datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model

Lilhore,

Dalal,

Varshney

et al. 2024

Sci Rep

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Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and ‘speech records’ of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD.

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Application of machine learning in predicting the risk of postpartum depression: A systematic review

Cited by 21 publications

References 51 publications

Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data

Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data

SVM-Based Model Combining Patients’ Reported Outcomes and Lymphocyte Phenotypes of Depression in Systemic Lupus Erythematosus

Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model

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