1,329
Views
24
CrossRef citations to date
0
Altmetric
Transformation of Mental Health & Brain Disorders Management

An insight into diagnosis of depression using machine learning techniques: a systematic review

&
Pages 749-771 | Received 03 Sep 2021, Accepted 02 Feb 2022, Published online: 17 Feb 2022

References

  • Nelson BD, Kessel EM, Klein DN, et al. Depression symptom dimensions and asymmetrical frontal cortical activity while anticipating reward. Psychophysiol. 2018;55(1):e12892.
  • Regier DA, Kuhl EA, Kupfer DJ. The DSM-5: classification and criteria changes. World Psychiatry. 2013;12(2):92–98.
  • World Health Organization, Depression; 2021. Available from: https://www.who.int/health-topics/depression#tab=tab_1
  • Wang X, Ren Y, Zhang W. Depression disorder classification of fMRI data using sparse low-rank functional brain network and graph-based features. Comput Math Methods Med. 2017;2017:1–11.
  • De Berardis D, Fornaro M, Valchera A, et al. Eradicating suicide at its roots: preclinical bases and clinical evidence of the efficacy of ketamine in the treatment of suicidal behaviors. IJMS. 2018;19(10):2888.
  • Ho RCM, Chua AC, Tran BX, et al. Factors associated with the risk of developing coronary artery disease in medicated patients with major depressive disorder. IJERPH. 2018;15(10):2073.
  • Orsolini L, Latini R, Pompili M, et al. Understanding the complex of suicide in depression: from research to clinics. Psychiatry Investig. 2020;17(3):207–221.
  • National Institute of Mental Health, Depression; 2021. Available from: https://www.nimh.nih.gov/health/topics/depression/index.shtml
  • Acharya UR, Oh SL, Hagiwara Y, et al. Automated EEG-based screening of depression using deep convolutional neural network. Comput Methods Programs Biomed. 2018;161:103–113.
  • Acharya UR, Sudarshan VK, Adeli H, et al. Computer-aided diagnosis of depression using EEG signals. Eur Neurol. 2015;73(5–6):329–336.
  • Guohou S, Lina Z, Dongsong Z. What reveals about depression level? The role of multimodal features at the level of interview questions. Inf Manag. 2020;57(7):103349.
  • Ho RC, Mak KK, Chua AN, et al. The effect of severity of depressive disorder on economic burden in a university hospital in Singapore. Expert Rev Pharmacoecon Outcomes Res. 2013;13(4):549–559.
  • Victor E, Aghajan ZM, Sewart AR, et al. Detecting depression using a framework combining deep multimodal neural networks with a purpose-built automated evaluation. Psychol Assess. 2019;31(8):1019–1027.
  • Husain SF, Yu R, Tang TB, et al. Validating a functional near-infrared spectroscopy diagnostic paradigm for major depressive disorder. Sci Rep. 2020;10(1):1–9.
  • Ho CSH, Lim LJH, Lim AQ, et al. Diagnostic and predictive applications of functional near-Infrared spectroscopy for major depressive disorder: a systematic review. Front Psychiatry. 2020;11:378.
  • Tran BX, McIntyre RS, Latkin CA, et al. The current research landscape on the artificial intelligence application in the management of depressive disorders: a bibliometric analysis. IJERPH. 2019;16(12):2150.
  • Priya A, Garg S, Tigga NP. Predicting anxiety, depression and stress in modern life using machine learning algorithms. Procedia Comput Sci. 2020;167:1258–1267.
  • Zogan H, Wang X, Jameel S, et al. Depression detection with multi-modalities using a hybrid deep learning model on social media. arXiv Preprint arXiv:2007.02847. 2020.
  • Zhu G, Jiang B, Tong L, et al. Applications of deep learning to neuro-imaging techniques. Front Neurol. 2019;10:869.
  • Wan Z, Yang R, Huang M, et al. A review on transfer learning in EEG signal analysis. Neurocomputing. 2021;421:1–14.
  • Ramasubbu R, Brown EC, Marcil LD, et al. Automatic classification of major depression disorder using arterial spin labeling MRI perfusion measurements. Psychiatry Clin Neurosci. 2019;73(8):486–493.
  • Byeon H. Development of a depression in Parkinson's disease prediction model using machine learning. World J Psychiatry. 2020;10(10):234–244.
  • Kasthurirathne SN, Biondich PG, Grannis SJ, et al. Identification of patients in need of advanced care for depression using data extracted from a statewide health information exchange: a machine learning approach. J Med Internet Res. 2019;21(7):e13809.
  • Richter T, Fishbain B, Markus A, et al. Using machine learning-based analysis for behavioral differentiation between anxiety and depression. Sci Rep. 2020;10(1):1–12.
  • Zheng H, Zheng P, Zhao L, et al. Predictive diagnosis of major depression using NMR-based metabolomics and least-squares support vector machine. Clin Chim Acta. 2017;464:223–227.
  • Kim EY, Lee MY, Kim SH, et al. Diagnosis of major depressive disorder by combining multimodal information from heart rate dynamics and serum proteomics using machine-learning algorithm. Prog Neuropsychopharmacol Biol Psychiatry. 2017;76:65–71.
  • Cui L, Wang C, Wu Z, et al. Symptomatology differences of major depression in psychiatric versus general hospitals: a machine learning approach. J Affect Disord. 2020;260:349–360.
  • Yu JS, Xue AY, Redei EE, et al. A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder. Transl Psychiatry. 2016;6(10):e931.
  • Yi Z, Li Z, Yu S, et al. Blood-based gene expression profiles models for classification of subsyndromal symptomatic depression and major depressive disorder. PLOS One. 2012;7(2):e31283.
  • Dinga R, Marquand AF, Veltman DJ, et al. Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach. Transl Psychiatry. 2018;8(1):1–11.
  • de Souza Filho EM, Rey HCV, Frajtag RM, et al. Can machine learning be useful as a screening tool for depression in primary care? J Psychiatric Res. 2021;132:1–6.
  • Ucuz I, Ari A, Ozcan OO, et al. Estimation of the development of depression and PTSD in children exposed to sexual abuse and development of decision support systems by using artificial intelligence. J Child Sex Abus. 2020;1–13. DOI:https://doi.org/10.1080/10538712.2020.1841350
  • McGinnis RS, McGinnis EW, Hruschak J, et al. Rapid anxiety and depression diagnosis in young children enabled by wearable sensors and machine learning. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2018 Jul 17–21; Honolulu. 2018. IEEE. p. 3983–3986.
  • Choi, J., Choi, J., & Choi, W. J. Predicting depression among community residing older adults: a use of machine learning approch. In: Nursing Informatics 2018. IOS Press; 2018. p. 265–265.
  • Song X, Zhang Z, Zhang R, et al. Predictive markers of depression in hypertension. Medicine. 2018;97(32):e11768.
  • Xu Z, Zhang Q, Li W, et al. Individualized prediction of depressive disorder in the elderly: a multitask deep learning approach. Int J Med Inform. 2019;132:103973.
  • Cho SE, Geem ZW, Na KS. Prediction of depression among medical check-ups of 433,190 patients: a nationwide population-based study. Psychiatry Res. 2020;293:113474.
  • Arun V, Prajwal V, Krishna M, et al. Neural network in a projection learning framework for depression classification in MYNAH cohort. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI). USA: IEEE; 2018. p. 25–32.
  • Guo W, Cui X, Liu F, et al. Decreased interhemispheric coordination in the posterior default-mode network and visual regions as trait alterations in first-episode, drug-naive major depressive disorder. Brain Imaging Behav. 2018;12(5):1251–1258.
  • Mwangi B, Ebmeier KP, Matthews K, et al. Multi-Centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder. Brain. 2012;135(Pt 5):1508–1521.
  • Kipli K, Kouzani AZ. An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka International Convention Center, Osaka. IEEE; 2013. p. 1382–1385.
  • Hong S, Liu YS, Cao B, et al. Identification of suicidality in adolescent major depressive disorder patients using sMRI: a machine learning approach. J Affect Disord. 2021;280(Pt A):72–76.
  • Foland-Ross LC, Sacchet MD, Prasad G, et al. Cortical thickness predicts the first onset of major depression in adolescence. Int J Dev Neurosci. 2015;46:125–131.
  • Kim D, Kang P, Kim J, et al. Machine learning classification of first-onset drug-naïve MDD using structural MRI. IEEE Access. 2019;7:153977–153985.
  • Wu MJ, Wu HE, Mwangi B, et al. Prediction of pediatric unipolar depression using multiple neuromorphometric measurements: a pattern classification approach. J Psychiatr Res. 2015;62:84–91.
  • Nakano T, Takamura M, Ichikawa N, et al. Enhancing multi-center generalization of machine learning-based depression diagnosis from resting-state fMRI. Front Psychiatry. 2020;11:400.
  • Yu H, Li ML, Li YF, et al. Anterior cingulate cortex, insula and amygdala seed-based whole brain resting-state functional connectivity differentiates bipolar from unipolar depression. J Affect Disord. 2020;274:38–47.
  • Guo H, Qin M, Chen J, et al. Machine-learning classifier for patients with major depressive disorder: multifeature approach based on a high-order minimum spanning tree functional brain network. Comput Math Methods Med. 2017;2017:1–14.
  • Chen X, Liu C, He H, et al. Transdiagnostic differences in the resting-state functional connectivity of the prefrontal cortex in depression and schizophrenia. J Affect Disord. 2017;217:118–124.
  • Grotegerd D, Suslow T, Bauer J, et al. Discriminating unipolar and bipolar depression by means of fMRI and pattern classification: a pilot study. Eur Arch Psychiatry Clin Neurosci. 2013;263(2):119–131.
  • Bhaumik R, Jenkins LM, Gowins JR, et al. Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity. NeuroImage: Clinical. 2017;16:390–398.
  • Yu Y, Shen H, Zeng LL, et al. Convergent and divergent functional connectivity patterns in schizophrenia and depression. PLOS One. 2013;8(7):e68250.
  • Mourão‐Miranda J, Almeida JR, Hassel S, et al. Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression. Bipolar Disorders. 2012;14(4):451–460.
  • Yan B, Xu X, Liu M, et al. Quantitative identification of major depression based on resting-state dynamic functional connectivity: a machine learning approach. Front Neurosci. 2020;14:191.
  • Cao L, Guo S, Xue Z, et al. Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis. Psychiatry Clin Neurosci. 2014;68(2):110–119.
  • Sundermann B, Feder S, Wersching H, et al. Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample. J Neural Transm. 2017;124(5):589–605.
  • Wei M, Qin J, Yan R, et al. Identifying major depressive disorder using hurst exponent of resting-state brain networks. Psychiatry Res. 2013;214(3):306–312.
  • Lord A, Horn D, Breakspear M, et al. Changes in community structure of resting state functional connectivity in unipolar depression; 2012.
  • Shimizu Y, Yoshimoto J, Toki S, et al. Toward probabilistic diagnosis and understanding of depression based on functional MRI data analysis with logistic group LASSO. PLOS One. 2015;10(5):e0123524.
  • Sato JR, Moll J, Green S, et al. Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression. Psychiatry Res. 2015;233(2):289–291.
  • Guo H, Cao X, Liu Z, et al. Machine learning classifier using abnormal brain network topological metrics in major depressive disorder. Neuroreport. 2012;23(17):1006–1011.
  • Chun JY, Sendi MS, Sui J, et al. Visualizing Functional Network Connectivity Difference between Healthy Control and Major Depressive Disorder Using an Explainable Machine-learning Method. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC); 2020. Montreal: IEEE. p. 1424–1427.
  • Yoshida K, Shimizu Y, Yoshimoto J, et al. Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression. PLOS One. 2017;12(7):e0179638.
  • Shao J, Dai Z, Zhu R, et al. Early identification of bipolar from unipolar depression before manic episode: evidence from dynamic rfMRI. Bipolar Disord. 2019;21(8):774–784.
  • Yamashita A, Sakai Y, Yamada T, et al. Generalizable brain network markers of major depressive disorder across multiple imaging sites. PLOS Biol. 2020;18(12):e3000966.
  • Ramasubbu R, Brown MR, Cortese F, et al. Accuracy of automated classification of major depressive disorder as a function of symptom severity. Neuroimage Clin. 2016;12:320–331.
  • Gao S, Osuch EA, Wammes M, et al. Discriminating bipolar disorder from major depression based on kernel SVM using functional independent components. In: 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). Tokyo: IEEE; 2017. p. 1–6.
  • Chu SH, Lenglet C, Schreiner MW, et al. Anatomical biomarkers for adolescent major depressive disorder from diffusion weighted imaging using SVM classifier. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu: IEEE; 2018. p. 2740–2743.
  • Qin J, Wei M, Liu H, et al. Abnormal hubs of white matter networks in the frontal-parieto circuit contribute to depression discrimination via pattern classification. Magn Reson Imaging. 2014;32(10):1314–1320.
  • Qiu L, Huang X, Zhang J, et al. Characterization of major depressive disorder using a multiparametric classification approach based on high resolution structural images. J Psychiatry Neurosci. 2014;39(2):78–86.
  • Fang P, Zeng LL, Shen H, et al. Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging; 2012.
  • Vai B, Parenti L, Bollettini I, et al. Predicting differential diagnosis between bipolar and unipolar depression with multiple kernel learning on multimodal structural neuroimaging. Eur Neuropsychopharmacol. 2020;34:28–38.
  • Stolicyn A, Harris MA, Shen X, et al. Automated classification of depression from structural brain measures across two independent community-based cohorts. Hum Brain Mapp. 2020;41(14):3922–3937.
  • Schnyer DM, Clasen PC, Gonzalez C, et al. Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder. Psychiatry Res Neuroimaging. 2017;264:1–9.
  • Li X, Zhang X, Zhu J, et al. Depression recognition using machine learning methods with different feature generation strategies. Artif Intell Med. 2019;99:101696.
  • Mumtaz W, Qayyum A. A deep learning framework for automatic diagnosis of unipolar depression. Int J Med Inform. 2019;132:103983.
  • Mahato S, Goyal N, Ram D, et al. Detection of depression and scaling of severity using six channel EEG data. J Med Syst. 2020;44(7):1–12.
  • Shim M, Jin MJ, Im CH, et al. Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using P300 features. Neuroimage Clin. 2019;24:102001.
  • Hosseinifard B, Moradi MH, Rostami R. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Comput Methods Programs Biomed. 2013;109(3):339–345.
  • Liao SC, Wu CT, Huang HC, et al. Major depression detection from EEG signals using kernel eigen-filter-bank common spatial patterns. Sensors. 2017;17(6):1385.
  • Mumtaz W, Ali SSA, Yasin MAM, et al. A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). Med Biol Eng Comput. 2018;56(2):233–246.
  • Mumtaz W, Malik AS, Ali SSA, et al Detrended fluctuation analysis for major depressive disorder. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milano: IEEE; 2015. p. 4162–4165.
  • Erguzel TT, Tas C, Cebi M. A wrapper-based approach for feature selection and classification of major depressive disorder–bipolar disorders. Comput Biol Med. 2015;64:127–137.
  • Duan L, Duan H, Qiao Y, et al. Machine learning approaches for MDD detection and emotion decoding using EEG signals. Front Hum Neurosci. 2020;14:284.
  • Mahato S, Paul S. Classification of depression patients and normal subjects based on electroencephalogram (EEG) signal using alpha power and theta asymmetry. J Med Syst. 2020;44(1):1–8.
  • Uyulan C, Ergüzel TT, Unubol H, et al. Major depressive disorder classification based on different convolutional neural network models: deep learning approach. Clin EEG Neurosci. 2021;52(1):38–51.
  • Li X, Hu B, Shen J, et al. Mild depression detection of college students: an EEG-based solution with free viewing tasks. J Med Syst. 2015;39(12):1–6.
  • Kang M, Kwon H, Park JH, et al. Deep-asymmetry: asymmetry matrix image for deep learning method in pre-screening depression. Sensors. 2020;20(22):6526.
  • Čukić M, Stokić M, Simić S, et al. The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method. Cogn Neurodyn. 2020;14(4):443–455.
  • Cai H, Chen Y, Han J, et al. Study on feature selection methods for depression detection using three-electrode EEG data. Interdiscip Sci Comp Life Sci. 2018;10(3):558–565.
  • Mohammadi Y, Hajian M, Moradi MH. Discrimination of depression levels using machine learning methods on EEG signals. In: 2019 27th Iranian Conference on Electrical Engineering (ICEE). Yazd: IEEE. 2019. p. 1765–1769.
  • Sandheep P, Vineeth S, Poulose M, et al. Performance analysis of deep learning CNN in classification of depression EEG signals. In: TENCON 2019-2019 IEEE Region 10 Conference (TENCON). Kerala: IEEE; 2019. p. 1339–1344.
  • Mantri S, Patil D, Agrawal P, et al. Non invasive EEG signal processing framework for real time depression analysis. In: 2015 SAI Intelligent Systems Conference (IntelliSys). London: IEEE. 2015. p. 518–521.
  • Shen J, Zhang X, Huang X, et al. An optimal channel selection for EEG-based depression detection via Kernel-Target alignment. IEEE J Biomed Health Inform. 2021;25(7):2545–2556.
  • Bi K, Chattun MR, Liu X, et al. Abnormal early dynamic individual patterns of functional networks in low gamma band for depression recognition. J Affect Disord. 2018;238:366–374.
  • Lu Q, Bi K, Liu C, et al. Predicting depression based on dynamic regional connectivity: a windowed granger causality analysis of MEG recordings. Brain Res. 2013;1535:52–60.
  • Lu Q, Jiang H, Luo G, et al. Multichannel matching pursuit of MEG signals for discriminative oscillation pattern detection in depression. Int J Psychophysiol. 2013;88(2):206–212.
  • Jiang H, Dai Z, Lu Q, et al. Magnetoencephalography resting‐state spectral fingerprints distinguish bipolar depression and unipolar depression. Bipolar Disord. 2020;22(6):612–620.
  • Jiang H, Popov T, Jylänki P, et al. Predictability of depression severity based on posterior alpha oscillations. Clin Neurophysiol. 2016;127(4):2108–2114.
  • Roh T, Hong S, Yoo HJ. Wearable depression monitoring system with heart-rate variability. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago (IL): IEEE; 2014. p. 562–565.
  • Byun S, Kim AY, Jang EH, et al. Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol. Comput Biol Med. 2019;112:103381.
  • Byun S, Kim AY, Jang EH, et al. Entropy analysis of heart rate variability and its application to recognize major depressive disorder: a pilot study. Technol Health Care. 2019;27(S1):407–424.
  • Jakobsen P, Garcia-Ceja E, Riegler M, et al. Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls. PLOS One. 2020;15(8):e0231995.
  • Rodríguez-Ruiz JG, Galván-Tejada CE, Zanella-Calzada LA, et al. Comparison of night, day and 24 h motor activity data for the classification of depressive episodes. Diagnostics. 2020;10(3):162.
  • Qian K, Kuromiya H, Zhang Z, et al. Teaching machines to know your depressive state: On physical activity in health and major depressive disorder. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin: IEEE; 2019. p. 3592–3595.
  • Mastoras RE, Iakovakis D, Hadjidimitriou S, et al. Touchscreen typing pattern analysis for remote detection of the depressive tendency. Scientific Reports,. 2019;9(1):1–12.
  • Gerych W, Agu E, Rundensteiner E. Classifying depression in imbalanced datasets using an autoencoder-based anomaly detection approach. In: 2019 IEEE 13th International Conference on Semantic Computing (ICSC). Newport Beach (CA): IEEE; 2019. p. 124–127.
  • Farhan AA, Yue C, Morillo R, et al. Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data. In: 2016 IEEE Wireless Health (WH). USA: IEEE. 2016. p. 1–8.
  • Razavi R, Gharipour A, Gharipour M. Depression screening using mobile phone usage metadata: a machine learning approach. J Am Med Inform Assoc. 2020;27(4):522–530.
  • Andrews JA, Harrison RF, Brown LJE, et al. Using the NANA toolkit at home to predict older adults' future depression. J Affect Disord. 2017;213:187–190.
  • Jiang H, Hu B, Liu Z, et al. Detecting depression using an ensemble logistic regression model based on multiple speech features. Comput Math Methods Med. 2018;2018:1–9.
  • Smith M, Dietrich BJ, Bai EW, et al. Vocal pattern detection of depression among older adults. Int J Ment Health Nurs. 2020;29(3):440–449.
  • McGinnis EW, Anderau SP, Hruschak J, et al. Giving voice to vulnerable children: machine learning analysis of speech detects anxiety and depression in early childhood. IEEE J Biomed Health Inform. 2019;23(6):2294–2301.
  • Sumali B, Mitsukura Y, Liang KC, et al. Speech quality feature analysis for classification of depression and dementia patients. Sensors. 2020;20(12):3599.
  • He L, Cao C. Automated depression analysis using convolutional neural networks from speech. J Biomed Inform. 2018;83:103–111.
  • Yalamanchili B, Kota NS, Abbaraju MS, et al. Real-time Acoustic based Depression Detection using Machine Learning Techniques. In: 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). VIT University, Vellore: IEEE. 2020. p. 1–6.
  • Lu X, Zhou A, Yang H. A novel method design for diagnosis of psychological symptoms of depression using speech analysis. In: 2017 International Conference on Orange Technologies (ICOT). Singapore: IEEE; 2017. p. 18–21.
  • Shukla, D. M., Sharma, K., & Gupta, S. Identifying Depression in a Person Using Speech Signals by Extracting Energy and Statistical Features. In: 2020 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). Bhopal: IEEE; 2020. p. 1–4.
  • Zhao Z, Bao Z, Zhang Z, et al. Automatic assessment of depression from speech via a hierarchical attention transfer network and attention autoencoders. IEEE J Sel Top Signal Process. 2020;14(2):423–434.
  • Scherer S, Lucas GM, Gratch J, et al. Self-reported symptoms of depression and PTSD are associated with reduced vowel space in screening interviews. IEEE Trans Affect Comp. 2015;7(1):59–73.
  • Ozkanca Y, Öztürk MG, Ekmekci MN, et al. Depression screening from voice samples of patients affected by Parkinson's disease. Digit Biomark. 2019;3(2):72–82.
  • Islam MR, Kabir MA, Ahmed A, et al. Depression detection from social network data using machine learning techniques. Health Inf Sci Syst. 2018;6(1):8–12.
  • Tlachac ML, Rundensteiner E. Screening for depression with retrospectively harvested private versus public text. IEEE J Biomed Health Inform. 2020;24(11):3326–3332.
  • Tlachac ML, Rundensteiner EA. Depression screening from text message reply latency. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Montreal: IEEE; 2020. p. 5490–5493.
  • Ricard BJ, Marsch LA, Crosier B, et al. Exploring the utility of community-generated social media content for detecting depression: an analytical study on Instagram. J Med Internet Res. 2018;20(12):e11817.
  • Hassan AU, Hussain J, Hussain M, et al. Sentiment analysis of social networking sites (SNS) data using machine learning approach for the measurement of depression. In: 2017 International Conference on Information and Communication Technology Convergence (ICTC). Jeju Island: IEEE; 2017. p. 138–140.
  • Tlachac ML, Toto E, Rundensteiner E. You're Making Me Depressed: Leveraging Texts from Contact Subsets to Predict Depression. In: 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). Chicago (IL): IEEE. 2019. p. 1–4.
  • Ding Y, Chen X, Fu Q, et al. A depression recognition method for college students using deep integrated support vector algorithm. IEEE Access. 2020;8:75616–75629.
  • Maglanoc LA, Kaufmann T, Jonassen R, et al. Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis. Hum Brain Mapp. 2020;41(1):241–255.
  • Bi K, Hua L, Wei M, et al. Dynamic functional-structural coupling within acute functional state change phases: evidence from a depression recognition study. J Affect Disord. 2016;191:145–155.
  • Sacchet MD, Prasad G, Foland-Ross LC, et al. Elucidating brain connectivity networks in major depressive disorder using classification-based scoring. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). Beijing: IEEE; 2016. p. 246–249.
  • Rive MM, Redlich R, Schmaal L, et al. Distinguishing medication-free subjects with unipolar disorder from subjects with bipolar disorder: state matters. Bipolar Disord. 2016;18(7):612–623.
  • Ding X, Yue X, Zheng R, et al. Classifying major depression patients and healthy controls using EEG, eye tracking and galvanic skin response data. J Affect Disord. 2019;251:156–161.
  • Feder S, Sundermann B, Wersching H, et al. Sample heterogeneity in unipolar depression as assessed by functional connectivity analyses is dominated by general disease effects. J Affect Disord. 2017;222:79–87.
  • Yang J, Zhang M, Ahn H, et al. Development and evaluation of a multimodal marker of major depressive disorder. Hum Brain Mapp. 2018;39(11):4420–4439.
  • Schultebraucks K, Yadav V, Shalev AY, et al. Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood. Psychol Med. 2020;1–11. DOI:https://doi.org/10.1017/S0033291720002718
  • Bhak Y, Jeong HO, Cho YS, et al. Depression and suicide risk prediction models using blood-derived multi-omics data. Transl Psychiatry. 2019;9:262.
  • Stolicyn A, Steele JD, Seriès P. Prediction of depression symptoms in individual subjects with face and eye movement tracking. Psychol Med. 2020;:1–9.
  • Niemann U, Brueggemann P, Boecking B, et al. Development and internal validation of a depression severity prediction model for tinnitus patients based on questionnaire responses and socio-demographics. Sci Rep. 2020;10(1):1–9.
  • Hilbert K, Lueken U, Muehlhan M, et al. Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: a multimodal machine learning study. Brain Behav. 2017;7(3):e00633.
  • Tomasik J, Han SYS, Barton-Owen G, et al. A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data. Transl Psychiatry. 2021;11(1):1–12.
  • Santana R, Santos B, Lima T, et al. Genetic algorithms for feature selection in the children and adolescents depression context. In: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). Boca Raton (FL): IEEE; 2019. p. 1470–1475.
  • Mahendran N, Vincent DR, Srinivasan K, et al. Sensor-assisted weighted average ensemble model for detecting major depressive disorder. Sensors. 2019;19(22):4822.
  • Sharma A, Verbeke WJ. Improving diagnosis of depression with XGBOOST machine learning model and a large biomarkers dutch dataset (n= 11,081). Front Big Data. 2020;3:15.
  • Tao X, Chi O, Delaney PJ, et al. Detecting depression using an ensemble classifier based on quality of life scales. Brain Inform. 2021;8(1):2–15.
  • Milak MS, Pantazatos S, Rashid R, et al. Higher 5-HT1A autoreceptor binding as an endophenotype for major depressive disorder identified in high risk offspring–a pilot study. Psychiatry Res Neuroimaging. 2018;276:15–23.
  • Liang S, Brown MR, Deng W, et al. Convergence and divergence of neurocognitive patterns in schizophrenia and depression. Schizophr Res. 2018;192:327–334.
  • Kim AY, Jang EH, Kim S, et al. Automatic detection of major depressive disorder using electrodermal activity. Sci Reports. 2018;8(1):1–9.
  • Sumali B, Mitsukura Y, Tazawa Y, et al. Facial landmark activity features for depression screening. In: 2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). Hiroshima: IEEE; 2019. p. 1376–1381.
  • McGinnis RS, McGinnis EW, Hruschak J, et al. Wearable sensors and machine learning diagnose anxiety and depression in young children. In: 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). Las Vegas (NV): IEEE; 2018. p. 410–413.
  • Joshi J, Dhall A, Goecke R, et al. Relative body parts movement for automatic depression analysis. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. Geneva: IEEE. 2013. p. 492–497.
  • Box plot representation; 2021. https://medium.com/dayem-siddiqui/understanding-and-interpreting-box-plots-d07aab9d1b6c.
  • Pearson correlation; 2021. https://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/11-correlation-and-regression.
  • Tukey test; 2021. https://en.wikipedia.org/wiki/Tukey%27s_range_test#The_studentized_range_(q)_distribution.
  • Tukey test; 2021. https://www.statisticshowto.com/tukey-test-honest-significant-difference/.
  • Squarcina L, Villa FM, Nobile M, et al. Deep learning for the prediction of treatment response in depression. J Affect Disord. 2021;281:618–622.
  • Hayward MD, Wallace RB. Wave 2 of the national social life, health, and aging project: an overview. J Gerontol Series B Psychol Sci Soc Sci. 2014;69(Suppl 2):S1–S3.
  • Cummins N, Scherer S, Krajewski J, et al. A review of depression and suicide risk assessment using speech analysis. Speech Commun. 2015;71:10–49.
  • William D, Suhartono D. Text-based depression detection on social media posts: a systematic literature review. Procedia Comput Sci. 2021;179:582–589.
  • Livingstone SR, Russo FA. The Ryerson Audio-Visual database of emotional speech and song (RAVDESS): a dynamic, multimodal set of facial and vocal expressions in North American English. PLOS One. 2018;13(5):e0196391.
  • Gratch J, Artstein R, Lucas GM, et al. The distress analysis interview corpus of human and computer interviews. Reykjavik: LREC; 2014. p. 3123–3128.
  • Bot BM, Suver C, Neto EC, et al. The mPower study, Parkinson disease mobile data collected using. ResearchKit Scientific Data. 2016;3(1):1–9.
  • Ringeval F, Schuller B, Valstar M, et al. Avec 2017: Real-life depression, and affect recognition workshop and challenge. In: Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge, USA; 2017. p. 3–9.
  • Garcia-Ceja E, Riegler M, Jakobsen P, et al. Depresjon: a motor activity database of depression episodes in unipolar and bipolar patients. In: Proceedings of the 9th ACM multimedia systems conference, New York; 2018. p. 472–477.
  • Cai H, Gao Y, Sun S, et al. MODMA dataset: a multi-modal open dataset for mental-disorder analysis. arXiv Preprint arXiv:2002.09283. 2020.
  • Dogrucu A, Perucic A, Isaro A, et al. Moodable: on feasibility of instantaneous depression assessment using machine learning on voice samples with retrospectively harvested smartphone and social media data. Smart Health. 2020;17:100118.
  • Lu Q, Jiang H, Bi K, et al. Discriminative analysis with a limited number of MEG trials in depression. J Affect Disord. 2014;167:207–214.
  • Behroozi M, Daliri MR, Boyaci H. Statistical analysis methods for the fMRI data. Basic Clin Neurosci. 2011;2(4):67–74.
  • Gao S, Calhoun VD, Sui J. Machine learning in major depression: from classification to treatment outcome prediction. CNS Neurosci Ther. 2018;24(11):1037–1052.
  • Zhuo C, Li G, Lin X, et al. The rise and fall of MRI studies in major depressive disorder. Transl Psychiatry. 2019;9(1):1–14.
  • Lai CY, Ho CS, Lim CR, et al. Functional near-infrared spectroscopy in psychiatry. BJPsych Adv. 2017;23(5):324–330.
  • Kumar CJ, Das PR. The diagnosis of ASD using multiple machine learning techniques. Int J Dev Disabil. 2021:1–11. DOI:https://doi.org/10.1080/20473869.2021.1933730
  • Dunlop, B. W., & Mayberg, H. S. (2017, November). Neuroimaging advances for depression. In: Cerebrum: the Dana forum on brain science (Vol. 2017). Dana Foundation.
  • Sharma M, Achuth PV, Deb D, et al. An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals. Cognit Syst Res. 2018;52:508–520.
  • de Aguiar Neto FS, Rosa JLG. Depression biomarkers using non-invasive EEG: a review. Neurosci Biobehav Rev. 2019;105:83–93.
  • Cai H, Qu Z, Li Z, et al. Feature-level fusion approaches based on multimodal EEG data for depression recognition. Information Fusion. 2020;59:127–138.
  • Tulay EE, Metin B, Tarhan N, et al. Multimodal neuroimaging: basic concepts and classification of neuropsychiatric diseases. Clin EEG Neurosci. 2019;50(1):20–33.
  • Shah D, Zheng W, Allen L, et al. Using a machine learning approach to investigate factors associated with treatment-resistant depression among adults with chronic non-cancer pain conditions and major depressive disorder. Curr Med Res Opin. 2021;37(5):847–859.
  • Crespo-Facorro B, Such P, Nylander AG, et al. The burden of disease in early schizophrenia – a systematic literature review. Curr Med Res Opin. 2021;37(1):109–121.
  • Pampouchidou A, Simos PG, Marias K, et al. Automatic assessment of depression based on visual cues: a systematic review. IEEE Trans Affective Comput. 2019;10(4):445–470.
  • Wu A, Scult MA, Barnes ED, et al. Smartphone apps for depression and anxiety: a systematic review and meta-analysis of techniques to increase engagement. NPJ Digit Med. 2021;4(1):20–29.
  • Bauer M, Glenn T, Geddes J, et al. Smartphones in mental health: a critical review of background issues, current status and future concerns. Int J Bipolar Disord. 2020;8(1):2–19.
  • Karlsdotter K, Bushe C, Hakkaart L, et al. Burden of illness and health care resource utilization in adult psychiatric outpatients with attention-deficit/hyperactivity disorder in Europe. Curr Med Res Opin. 2016;32(9):1547–1556.
  • Liu X, Liu Z, Wang G, et al. Ensemble transfer learning algorithm. IEEE Access. 2018;6:2389–2396.
  • Sharma, M.; Nath, K.; Sharma, R.K.; Kumar, C.J.; Chaudhary, A. Ensemble averaging of transfer learning models for identification of nutritional deficiency in rice plant. Electronics. 2022;11:148.
  • Čukić M, López V, Pavón J. Classification of depression through resting-state electroencephalogram as a novel practice in psychiatry: review. J Med Internet Res. 2020;22(11):e19548.
  • Guo H, Cheng C, Cao X, et al. Resting-state functional connectivity abnormalities in first-onset unmedicated depression. Neural Regen Res. 2014;9(2):153–163.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.