scholarly journals Tracking and Monitoring Mood Stability of Patients with Major Depressive Disorder by Machine Learning Models Using Passive Digital Data (Preprint)

10.2196/24365 ◽  
2020 ◽  
Author(s):  
Ran Bai ◽  
Le Xiao ◽  
Yu Guo ◽  
Xuequan Zhu ◽  
Nanxi Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Digital Data ◽  
Learning Models ◽  
Major Depressive ◽  
Machine Learning Models

scholarly journals Authorship Correction:Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study (Preprint)

2021 ◽  
Author(s):  
Ran Bai ◽  
Le Xiao ◽  
Yu Guo ◽  
Xuequan Zhu ◽  
Nanxi Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Multicenter Study ◽  
Digital Data ◽  
Learning Models ◽  
Major Depressive ◽  
Clinical Research Center ◽  
Medical University ◽  
Machine Learning Models

UNSTRUCTURED In “Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study” the authors noted two errors. In the originally published manuscript, there was no equal contribution footnote. This has been corrected to note that authors Ran Bai and Le Xiao all contributed equally to the manuscript. Additionally, the affiliation for authors Le Xiao, Xuequan Zhu, Nanxi Li, Lei Feng, Gang Wang was incorrectly listed as Beijing Anding Hospital, Capital Medical University The correct affiliation for these authors is: The National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University

scholarly journals Tracking and Monitoring Mood Stability of Patients with Major Depressive Disorder by Machine Learning Models Using Passive Digital Data (Preprint)

2020 ◽  
Author(s):  
Ran Bai ◽  
Le Xiao ◽  
Yu Guo ◽  
Xuequan Zhu ◽  
Nanxi Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Count Data ◽  
Step Count ◽  
Learning Models ◽  
Major Depressive ◽  
Mood Changes ◽  
Mood Swing ◽  
Machine Learning Models

BACKGROUND Major Depressive Disorder(MDD) is a common mental illness characterized by persistent sadness and a loss of interest in activities. Using smartphones and wearable devices to monitor MDD patients’ mental condition has been examined in several studies. However, there are few studies utilizing passively collected data to monitor mood changes in a time period. OBJECTIVE We aimed to examine the feasibility of monitoring mood status and stability of MDD patients using machine learning (ML) models trained by passively collected data including phone usage data, sleep data and step count data. METHODS We constructed 612 data samples representing time spans during 3 consecutive Patient Health Questionnaire-9(PHQ-9) assessments. Each data sample was labeled as Steady or Mood Swing with subgroups Steady-remission, Steady-depressed, Mood Swing-drastic, Mood Swing-moderate based on patients’ PHQ-9 scores from 3 visits. 252 features were extracted, and 4 feature selection models were applied. 6 different combinations of types of data were experimented using 6 different machine learning models. RESULTS A total of 334 participants with MDD were enrolled in this study. The highest accuracy of classification between Steady and Mood Swing was 76.62% and recall was 91.53% with features from all types of data being used. Among 6 combinations of types of data we experimented, the overall best combination was using Call Logs, Sleep data, Step count data and Heart rate data. The accuracies of predicting between Steady-remission and Mood Swing-drastic, Steady-remission and Mood Swing-moderate, Steady-depressed and Mood Swing-drastic were over 80%, and the accuracy of predicting between Steady-depressed and Mood Swing-moderate and the overall Steady to Mood Swing classification accuracy were over 75%. Comparing all 6 combinations above, we found that the overall prediction accuracies between Steady-remission and Mood Swing (drastic and moderate), are better than those between Steady-depressed and Mood Swing (drastic and moderate). CONCLUSIONS Our proposed method could be used to monitor MDD patients’ mood changes with a promising accuracy utilizing passively collected data, which can be used as a reference to doctors for adjusting treatment plans or a warning of relapse to patients and their guardians. CLINICALTRIAL Chinese Clinical Trial Registry (ChiCTR)(www.chictr.org.cn):ChiCTR1900021461

scholarly journals Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
P. M. Durai Raj Vincent ◽  
Nivedhitha Mahendran ◽  
Jamel Nebhen ◽  
N. Deepa ◽  
Kathiravan Srinivasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Common Mental Disorder ◽  
Classification Algorithms ◽  
It Professionals ◽  
Major Depressive ◽  
The Brain ◽  
Machine Learning Models ◽  
Better Than

Major depressive disorder (MDD) is the most common mental disorder in the present day as all individuals’ lives, irrespective of being employed or unemployed, is going through the depression phase at least once in their lifetime. In simple terms, it is a mood disturbance that can persist for an individual for more than a few weeks to months. In MDD, in most cases, the individuals do not consult a professional, and even if being consulted, the results are not significant as the individuals find it challenging to identify whether they are depressed or not. Depression, most of the time, cooccurs with anxiety and leads to suicide in few cases, among the employees, who are about to handle the pressure at work and home and mostly unnoticing such problems. This is why this work aims to analyze the IT employees who are mostly working with targets. The artificial neural network, which is modeled loosely like the brain, has proved in recent days that it can perform better than most of the classification algorithms. This study has implemented the multilayered neural perceptron and experimented with the backpropagation technique over the data samples collected from IT professionals. This study aims to develop a model that can classify depressed individuals from those who are not depressed effectively with the data collected from them manually and through sensors. The results show that deep-MLP with backpropagation outperforms other machine learning-based models for effective classification.

A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder

2013 ◽  
Vol 124 (10) ◽  
pp. 1975-1985 ◽  
Author(s):  
Ahmad Khodayari-Rostamabad ◽  
James P. Reilly ◽  
Gary M. Hasey ◽  
Hubert de Bruin ◽  
Duncan J. MacCrimmon
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Learning Approach ◽  
Major Depressive ◽  
Eeg Data ◽  
Machine Learning Approach ◽  
Ssri Treatment

scholarly journals Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods

2021 ◽  
Vol 15 ◽  
Author(s):  
Shu Zhao ◽  
Zhiwei Bao ◽  
Xinyi Zhao ◽  
Mengxiang Xu ◽  
Ming D. Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Meta Analysis ◽  
Single Gene ◽  
Support Vector ◽  
Svm Classifier ◽  
Diagnostic Model ◽  
Roc Curve Analysis ◽  
Major Depressive

BackgroundMajor depressive disorder (MDD) is a global health challenge that impacts the quality of patients’ lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes.Materials and MethodsWe collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package “MetaOmics” was applied for systematic meta-analysis of genome-wide expression data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of individual genes. To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection.ResultsOur analysis revealed six differentially expressed genes (AKR1C3, ARG1, KLRB1, MAFG, TPST1, and WWC3) with a false discovery rate (FDR) < 0.05 between MDD patients and control subjects. We then evaluated the diagnostic ability of these genes individually. With single gene prediction, we achieved a corresponding area under the curve (AUC) value of 0.63 ± 0.04, 0.67 ± 0.07, 0.70 ± 0.11, 0.64 ± 0.08, 0.68 ± 0.07, and 0.62 ± 0.09, respectively, for these genes. Next, we constructed the classifiers of SVM, RF, kNN, and NB with an AUC of 0.84 ± 0.09, 0.81 ± 0.10, 0.73 ± 0.11, and 0.83 ± 0.09, respectively, in validation datasets, suggesting that the SVM classifier might be superior for constructing an MDD diagnostic model. The final SVM classifier including 70 feature genes was capable of distinguishing MDD samples from healthy controls and yielded an AUC of 0.78 in an independent dataset.ConclusionThis study provides new insights into potential biomarkers through meta-analysis of GEO data. Constructing different machine learning models based on these biomarkers could be a valuable approach for diagnosing MDD in clinical practice.

Single-Channel EEG-Based Machine Learning Method for Prescreening Major Depressive Disorder

2019 ◽  
Vol 18 (05) ◽  
pp. 1579-1603 ◽  
Author(s):  
Zhijiang Wan ◽  
Hao Zhang ◽  
Jiajin Huang ◽  
Haiyan Zhou ◽  
Jie Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Single Channel ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Machine Learning Method ◽  
Learning Method ◽  
Eeg Analysis ◽  
Major Depressive

Many studies developed the machine learning method for discriminating Major Depressive Disorder (MDD) and normal control based on multi-channel electroencephalogram (EEG) data, less concerned about using single channel EEG collected from forehead scalp to discriminate the MDD. The EEG dataset is collected by the Fp1 and Fp2 electrode of a 32-channel EEG system. The result demonstrates that the classification performance based on the EEG of Fp1 location exceeds the performance based on the EEG of Fp2 location, and shows that single-channel EEG analysis can provide discrimination of MDD at the level of multi-channel EEG analysis. Furthermore, a portable EEG device collecting the signal from Fp1 location is used to collect the second dataset. The Classification and Regression Tree combining genetic algorithm (GA) achieves the highest accuracy of 86.67% based on leave-one-participant-out cross validation, which shows that the single-channel EEG-based machine learning method is promising to support MDD prescreening application.

GWAS-based machine learning approach to predict duloxetine response in major depressive disorder

2018 ◽  
Vol 99 ◽  
pp. 62-68 ◽  
Author(s):  
Malgorzata Maciukiewicz ◽  
Victoria S. Marshe ◽  
Anne-Christin Hauschild ◽  
Jane A. Foster ◽  
Susan Rotzinger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Learning Approach ◽  
Major Depressive ◽  
Machine Learning Approach
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