Deep learning in mental health outcome research: a scoping review
Open accessAbstract
Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual’s physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients’ historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.
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The "black box" nature of some ML models raises concerns about their transparency and accountability, particularly in the context of public health interventions that can have far-reaching consequences.
“This “black box” nature of some ML models raises concerns about their transparency and accountability, particularly in the context of public health interventions that can have far-reaching consequences [55, 56].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [56]
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Pinaya, W. H. L., Mechelli, A., & Sato, J. R. (2018) · Human Brain Mapping · doi:10.1002/hbm.24423 · Cited by 137 · see citing works
EEG-based mild depression recognition using convolutional neural network
Li, X., La, R., Wang, Y., Niu, J., Zeng, S., Sun, S., & Zhu, J. (2019) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-019-01959-2 · Cited by 136 · cited 1× · see citing works
“Li et al. 46 presented a computer-aided detection system by extracting multiple types of information (e.g., spectral, spatial, and temporal information) to recognize mild depression based on CNN architecture.”
BackgroundSong, S., Shen, L., & Valstar, M. (2018) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2018.00032 · Cited by 133 · cited 1× · see citing works
“For example, several selected studies proposed to predict symptom severity scores 56,57,77,82,84,87,89 .”
BackgroundTechnology and mental health: The role of artificial intelligence
Lovejoy, C. A. (2019) · European Psychiatry · doi:10.1016/j.eurpsy.2018.08.004 · Cited by 129 · see citing works
Cho, Y., Bianchi‐Berthouze, N., & Julier, S. (2018) · UCL Discovery (University College London) · Cited by 129 · see citing works
The use of electronic health records for psychiatric phenotyping and genomics
Smoller, J. W. (2017) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32548 · Cited by 125 · see citing works
Cho, Y., Bianchi‐Berthouze, N., & Julier, S. (2017) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2017.8273639 · Cited by 124 · cited 1× · see citing works
“In addition, thermal images that track persons ’ breathing patterns were also fed to a deep model to estimate psychological stress level (mental overload) 93 .”
AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and ChallengePreprint
Valstar, M., Gratch, J. S., Schuller, B. W., Ringeval, F., Lalanne, D., Torres, M. T., Scherer, S. J., Stratou, G., Cowie, R., & Pantić, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.01600 · Cited by 121 · cited 1× · see citing works
“…to elicit facial expressions and speech responses of the subjects of bipolar disorder, unipolar depression, and healthy controls; 72 and (ii) the International Audio/Visual Emotion Recognition Challenges (AVEC) depression dataset 73 – 75 , collected within human – computer interaction scenario.”
BackgroundArtificial neural network for classification of depressive and normal in EEG
Mohan, Y., Chee, S. S., Xin, D. K. P., & Foong, L. P. (2016) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes.2016.7843459 · Cited by 118 · see citing works
Challenges in Representation Learning: A report on three machine learning contestsPreprint
Goodfellow, I., Erhan, D., Carrier, P. L., Courville, A. C., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D., et al. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1307.0414 · Cited by 114 · cited 1× · see citing works
“Other studies 83,84 pre-trained CNN over general facial expression datasets 96,97 for extracting face appearance features.”
ResultTran, T., & Kavuluru, R. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.06.010 · Cited by 101 · see citing works
Psychological stress detection from cross-media microblog data using Deep Sparse Neural Network
Lin, H., Jia, J., Guo, Q., Xue, Y., Jie, H. J., Cai, L., & Feng, L. (2014) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2014.6890213 · Cited by 90 · see citing works
Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data
Jaiswal, S., Valstar, M., Gillott, A., & Daley, D. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.95 · Cited by 85 · see citing works
Choi, S. B., Lee, W., Yoon, J., Won, J., & Kim, D. W. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.01.019 · Cited by 85 · see citing works
Classification on ADHD with Deep Learning
Kuang, D., & He, L.-H. (2014) · International Conference on Cloud Computing and Big Data · doi:10.1109/ccbd.2014.42 · Cited by 78 · cited 6× · see citing works
“They reported accuracy over 0.93 in the diagnostic prediction of ADHD and ASD by using the CNN architecture.”
MethodInfluentialX-A-BiLSTM: a Deep Learning Approach for Depression Detection in Imbalanced Data
Cong, Q., Feng, Z., Li, F., Xiang, Y., Rao, G., & Tao, C. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621230 · Cited by 73 · cited 1× · see citing works
“In this study, the articles that used DL to analyze social media data mainly focused on stress detection 98 – 101 , depression identi fi cation 102 – 106 , and estimation of suicide risk 103,105,107 – 109 .”
MethodGeraci, J., Wilansky, P., Luca, V. D., Roy, A., Kennedy, J. L., & Strauss, J. (2017) · Evidence-Based Mental Health · doi:10.1136/eb-2017-102688 · Cited by 72 · see citing works
Ive, J., Gkotsis, G., Dutta, R., Stewart, R. J., & Velupillai, S. U. (2018) · Workshop on Computational Linguistics and Clinical Psychology (CLPsych) · doi:10.18653/v1/w18-0607 · Cited by 65 · cited 2× · see citing works
“In this study, the articles that used DL to analyze social media data mainly focused on stress detection 98 – 101 , depression identi fi cation 102 – 106 , and estimation of suicide risk 103,105,107 – 109 .”
BackgroundMethodDeep fMRI: AN end-to-end deep network for classification of fMRI data
Riaz, A., Asad, M., Arif, S. M. M. R. A., Alonso, E., Dima, D., Corr, P. J., & Slabaugh, G. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363838 · Cited by 59 · cited 1× · see citing works
“Among the studies reviewed in this study, three 29,30,37 reported to involve CNN layers and achieved desirable performances.”
BackgroundHuang, K.-Y., Wu, C., & Su, M.-H. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.12.016 · Cited by 59 · see citing works
Discrimination of ADHD Based on fMRI Data with Deep Belief Network
Kuang, D., Guo, X.-J., An, X., Zhao, Y., & He, L.-H. (2014) · International Conference on Intelligent Computing · doi:10.1007/978-3-319-09330-7_27 · Cited by 56 · see citing works
Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis
Matsubara, T., Tashiro, T., & Uehara, K. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2895663 · Cited by 54 · cited 1× · see citing works
“Also, several studies analyzed sMRIs to investigate schizophrenia 32 – 36 , where DFNN, DBN, and autoencoder were utilized.”
BackgroundMulti task sequence learning for depression scale prediction from video
Chao, L., Tao, J., Yang, M., & Li, Y. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344620 · Cited by 53 · cited 5× · see citing works
“In particular, CNNs were leveraged to encode the temporal and spectral features from the voice signals 76 – 80 and static facial or physical expression features from the video frames 79,81 – 84 .”
BackgroundMethodResultInfluentialA Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks
Grün, F., Rupprecht, C., Navab, N., & Tombari, F. (2016) · arXiv · doi:10.48550/arxiv.1606.07757 · Cited by 49 · see citing works
DeepBipolar: Identifying genomic mutations for bipolar disorder via deep learning
Sundaram, L., Bhat, R. R., Viswanath, V., & Li, X. (2017) · Human Mutation · doi:10.1002/humu.23272 · Cited by 49 · see citing works
Drug knowledge bases and their applications in biomedical informatics research
Zhu, Y., Elemento, O., Pathak, J., & Wang, F. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx169 · Cited by 49 · cited 1× · see citing works
“The existing biomedical knowledge bases are invaluable sources for solving healthcare problems 133,134 .”
BackgroundSelf-citationDCNN and DNN based multi-modal depression recognition
Yang, L., Jiang, D., Han, W., & Hichem Sahli, B. (2017) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2017.8273643 · Cited by 46 · cited 2× · see citing works
“In particular, CNNs were leveraged to encode the temporal and spectral features from the voice signals 76 – 80 and static facial or physical expression features from the video frames 79,81 – 84 .”
BackgroundMethodDepression Severity Classification from Speech Emotion
Harati, S., Crowell, A. L., Mayberg, H. S., & Nemati, S. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513610 · Cited by 44 · see citing works
Question Answering for Suicide Risk Assessment Using Reddit
Alambo, A., Gaur, M., Lokala, U., Kursuncu, U., Thirunarayan, K., Gyrard, A., Sheth, A., Welton, R. S., & Pathak, J. (2019) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icosc.2019.8665525 · Cited by 43 · cited 1× · see citing works
“In this study, the articles that used DL to analyze social media data mainly focused on stress detection 98 – 101 , depression identi fi cation 102 – 106 , and estimation of suicide risk 103,105,107 – 109 .”
MethodSelf-citationThe Biological Sciences Curriculum Study.
Mayer, W. V. (1970) · Cited by 40 · see citing works
Online Social Networks in Health Care: A Study of Mental Disorders on Reddit
Fraga, B. S., da Silva, A. P. C., & Murai, F. (2018) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1109/wi.2018.00-36 · Cited by 40 · cited 1× · see citing works
“In this study, the articles that used DL to analyze social media data mainly focused on stress detection 98 – 101 , depression identi fi cation 102 – 106 , and estimation of suicide risk 103,105,107 – 109 .”
MethodRios, A., & Kavuluru, R. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.05.008 · Cited by 31 · see citing works
Ulloa, A., Plis, S., Erhardt, E. B., & Calhoun, V. D. (2015) · Machine learning for signal processing ... · doi:10.1109/mlsp.2015.7324379 · Cited by 26 · cited 1× · see citing works
“As reported in some selected studies 28,31,35,37 , the DL models can bene fi t from feature engineering techniques and have been shown to outperform the traditional ML models in the prediction of multiple conditions such as depression, schizophrenia, and ADHD.”
ResultThe Facial Stress Recognition Based on Multi-histogram Features and Convolutional Neural Network
Prasetio, B. H., Tamura, H., & Tanno, K. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00157 · Cited by 24 · cited 1× · see citing works
“Prasetio et al. 91 identi fi ed the stress types (e.g., neutral, low stress, and high stress) from facial frontal images.”
BackgroundSu, M.-H., Wu, C., Huang, K.-Y., & Yang, T. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2899884 · Cited by 24 · cited 1× · see citing works
“Autoencoders were used to learn low-dimensional representations for people ’ s vocal 85,86 and visual expression 87,88 , and RNNs were engaged to characterize the temporal evolution of emotion based on the CNN-learned features and/or other handcraft features 76,81,84 – 90 .”
MethodDawood, A. A., Turner, S., & Perepa, P. (2018) · IEEE Access · doi:10.1109/access.2018.2879619 · Cited by 23 · see citing works
Deep learning based automatic diagnoses of attention deficit hyperactive disorder
Zou, L., Zheng, J., & McKeown, M. J. (2017) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2017.8309103 · Cited by 20 · cited 2× · see citing works
“For example, Zou et al.(28) developed a multimodal model composed of two CNNs for modeling fMRI and sMRI modalities, respectively.”
BackgroundDai, H.-J., & Jonnagaddala, J. (2018) · PLoS ONE · doi:10.1371/journal.pone.0204493 · Cited by 17 · cited 2× · see citing works
“For example, several selected studies proposed to predict symptom severity scores 56,57,77,82,84,87,89 .”
BackgroundMethodKhan, A., Liu, Q., & Wang, K. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2469-7 · Cited by 16 · cited 1× · see citing works
“To further prioritize susceptibility genes, they designed another deep model, iMEGES 68 , which integrates the ncDeepBrain score, general gene scores, and disease-speci fi c scores for estimating gene risk.”
Improvement on Speech Depression Recognition Based on Deep Networks
Li, J., Fu, X., Shao, Z., & Shang, Y. (2018) · Chinese Automation Congress · doi:10.1109/cac.2018.8623055 · Cited by 16 · see citing works
Application of Autoencoder in Depression Diagnosis
Xiang-Fei, G. E. N. G., & Xu, J. (2017) · DEStech Transactions on Computer Science and Engineering · doi:10.12783/dtcse/csma2017/17335 · Cited by 15 · cited 3× · see citing works
“Geng et al. 37 proposed to use CNN and autoencoder to acquire meaningful features from the original time series of fMRI data for predicting depression.”
BackgroundMethodResultDetection of mood disorder using speech emotion profiles and LSTM
Yang, T., Wu, C., Huang, K.-Y., & Su, M.-H. (2016) · International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp.2016.7918439 · Cited by 14 · cited 1× · see citing works
“Autoencoders were used to learn low-dimensional representations for people ’ s vocal 85,86 and visual expression 87,88 , and RNNs were engaged to characterize the temporal evolution of emotion based on the CNN-learned features and/or other handcraft features 76,81,84 – 90 .”
MethodFarzi, S., Kianian, S., & Rastkhadive, I. (2017) · International Symposium on Computational and Business Intelligence (ISCBI) · doi:10.1109/iscbi.2017.8053552 · Cited by 13 · see citing works
Correlating Stressor Events for Social Network Based Adolescent Stress Prediction
Li, Q., Zhao, L., Xue, Y., Li, J., Alli, M., & Feng, L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-55753-3_40 · Cited by 11 · see citing works
An Affect Prediction Approach Through Depression Severity Parameter Incorporation in Neural Networks
Gupta, R., Sahu, S., Espy-Wilson, C., & Narayanan, S. S. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-120 · Cited by 10 · cited 1× · see citing works
“For example, Gupta et al. 94 designed a DFNN for affective prediction from audio and video modalities.”
MethodExploring microscopic fluctuation of facial expression for mood disorder classification
Su, M.-H., Wu, C., Huang, K.-Y., Hong, Q.-B., & Wang, H. (2017) · International Conference on Orange Technologies · doi:10.1109/icot.2017.8336090 · Cited by 10 · see citing works
Data collection of elicited facial expressions and speech responses for mood disorder detection
Huang, K.-Y., Wu, C., Kuo, Y.-T., Yen, H.-H., Jang, F.-L., & Chiu, Y. (2015) · International Conference on Orange Technologies · doi:10.1109/icot.2015.7498502 · Cited by 7 · cited 1× · see citing works
“…emotional videos to elicit facial expressions and speech responses of the subjects of bipolar disorder, unipolar depression, and healthy controls; 72 and (ii) the International Audio/Visual Emotion Recognition Challenges (AVEC) depression dataset 73 – 75 , collected within human – computer…”
BackgroundA deep learning based scoring system for prioritizing susceptibility variants for mental disorders
Khan, A., & Wang, K. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217916 · Cited by 7 · cited 2× · see citing works
“Wang et al. 69 developed a novel deep architecture that combines deep Boltzmann machine architecture 70 with conditional and lateral connections derived from the gene regulatory network.”
BackgroundMood disorder identification using deep bottleneck features of elicited speech
Huang, K.-Y., Wu, C., Su, M.-H., & Chou, C.-H. (2017) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2017.8282296 · Cited by 5 · cited 1× · see citing works
“Autoencoders were used to learn low-dimensional representations for people ’ s vocal 85,86 and visual expression 87,88 , and RNNs were engaged to characterize the temporal evolution of emotion based on the CNN-learned features and/or other handcraft features 76,81,84 – 90 .”
Method
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Prefrontal cortex and depression
Pizzagalli, D. A., & Roberts, A. C. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-021-01101-7 · Cited by 644 · see citing works
“Finally, we believe embracing a computational psychiatry approach – including the use of deep learning and artificial neural network to diagnose MDD [266, 267] and predict treatment response [268] – offers unprecedented opportunities, particularly when coupled with improved experimental design to isolate precise psych…”
BackgroundNatural language processing applied to mental illness detection: a narrative review
Zhang, T., Schoene, A. M., Ji, S., & Ananiadou, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00589-7 · Cited by 476 · see citing works
“For the task of mental illness detection from text, deep learning techniques have recently attracted more attention and shown better performance compared to machine learning ones 116 .”
MethodFrom promise to practice: towards the realisation of AI-informed mental health care
Koutsouleris, N., Hauser, T. U., Skvortsova, V., & De Choudhury, M. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00153-4 · Cited by 251 · see citing works
Machine learning model to predict mental health crises from electronic health records
Garriga, R., Mas, J., Abraha, S., Nolan, J., Harrison, O., Tadros, G., & Matic, A. (2022) · Nature Medicine · doi:10.1038/s41591-022-01811-5 · Cited by 229 · see citing works
MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare
Ji, S., Zhang, T., Ansari, L., Fu, J., Tiwari, P., & Cambria, E. (2022) · Proceedings of the Language Resources and Evaluation Conference · doi:10.63317/2d7umxifj8wz · Cited by 233 · see citing works
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
Cui, H., Dai, W., Zhu, Y., Kan, X., Gu, A. A. C., Lukemire, J., Zhan, L., He, L., Guo, Y., & Yang, C. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3218745 · Cited by 218 · see citing works
“In recent years, the analysis of brain networks has become increasingly important in neuroimaging studies to understand human brain organization across different groups of individuals [19–23].”
Modern views of machine learning for precision psychiatry
Chen, Z. S., Kulkarni, P., Galatzer‐Levy, I. R., Bigio, B., Nasca, C., & Zhang, Y. (2022) · Patterns · doi:10.1016/j.patter.2022.100602 · Cited by 225 · see citing works
“The past two decades have witnessed substantial growth of ML applications for psychiatry in the literature, reflected in many applications and reviews [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20].”
BackgroundSu, C., Aseltine, R. H., Doshi, R., Chen, K., Rogers, S. C., & Wang, F. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-01100-0 · Cited by 163 · see citing works
Self-citationArtificial intelligence in psychiatry research, diagnosis, and therapy
Sun, J., Dong, Q., Wang, S.-W., Zheng, Y., Liu, X., Lu, T., Yuan, K., Shi, J., Hu, B., Hu, B., et al. (2023) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2023.103705 · Cited by 151 · see citing works
Automatic sleep staging of EEG signals: recent development, challenges, and future directions
Phan, H., & Mikkelsen, K. B. (2022) · Physiological Measurement · doi:10.1088/1361-6579/ac6049 · Cited by 145 · see citing works
“…(Ting et al 2018), clinical outcome prediction (Rajkomar et al 2018), automatic ECG analysis (Ribeiro et al 2020), automatic sleep analysis (Stephansen et al 2018), mental health screening (Su et al 2020), intelligent assistive technologies for dementia care (Ienca et al 2017), tomention a few.”
BackgroundTejaswini, V., Babu, K. S., & Sahoo, B. (2022) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3569580 · Cited by 139 · see citing works
“The advent of Deep Neural networks [35,36] has shown its mark in various text processing, speech, and image analysis tasks.”
BackgroundAlhuwaydi, A. M. (2024) · Risk Management and Healthcare Policy · doi:10.2147/rmhp.s461562 · Cited by 152 · see citing works
Large language models for disease diagnosis: a scoping review
Zhou, S., Xu, Z., Zhang, M., Xu, C., Guo, Y., Zhan, Z., Fang, Y., Ding, S., Wang, J., Xu, K., et al. (2025) · npj Artificial Intelligence · doi:10.1038/s44387-025-00011-z · Cited by 143 · see citing works
Self-citationKhare, S. K., March, S., Barua, P. D., Gadre, V. M., & Acharya, U. R. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101898 · Cited by 113 · see citing works
Application of Artificial Intelligence on Psychological Interventions and Diagnosis: An Overview
Zhou, S., Zhao, J., & Zhang, L. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.811665 · Cited by 128 · see citing works
“Recent AI studies suggested promising directions for employing technology including deep learning in improving our understanding of mental health diagnosis and treatment (2, 3).”
Toward explainable AI (XAI) for mental health detection based on language behavior
Kerz, E., Zanwar, S., Qiao, Y., & Wiechmann, D. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1219479 · Cited by 121 · see citing works
“Although the field of MIDAS has made significant progress, recent studies have prioritized predictive accuracy over model interpretability by relying on deep learning techniques and architectures, as noted by Su et al. (23), Zhang et al. (11), and Greco et al. (24).”
MethodLee, S., Hussein, R., Ward, R. K., Wang, Z. J., & McKeown, M. J. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109282 · Cited by 103 · see citing works
Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review
Brendel, M., Su, C., Bai, Z., Zhang, H., Elemento, O., & Wang, F. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.11.011 · Cited by 102 · see citing works
“…high-dimensional heterogeneous multi-omics feature space, and the flexible architecture that can model each modality separately using small DNNs ( e.g. , autoencoders) and combine them later to aggregate information extracted from each modality appropriately to learn a joint representation [170].”
BackgroundSelf-citationKim, A. Y., Jang, E. H., Lee, S., Choi, K., Park, J. G., & Shin, H. (2022) · Journal of Medical Internet Research · doi:10.2196/34474 · Cited by 97 · see citing works
“…deep learning techniques have achieved high success in audio and video recognition tasks, and many studies have reported that deep learning approaches in depression detection have significantly improved performance compared with conventional approaches that use partial representation [27].”
Abd-Alrazaq, A. A., Alhuwail, D., Schneider, J., Toro, C. T., Ahmed, A., Alzubaidi, M. S., Alajlani, M., & Househ, M. S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00631-8 · Cited by 90 · see citing works
Depression diagnosis by deep learning using EEG signals: A systematic review
Safayari, A., & Bolhasani, H. (2021) · Medicine in Novel Technology and Devices · doi:10.1016/j.medntd.2021.100102 · Cited by 87 · see citing works
Emotion fusion for mental illness detection from social media: A survey
Zhang, T., Yang, K., Ji, S., & Ananiadou, S. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.11.031 · Cited by 91 · see citing works
“A good model should not only exhibit high levels of performance, but should also possess strong interpretability, since this can be valuable in guiding psychiatrists to better understand how certain textual features can be indicative of mental illness [25].”
BackgroundAdler, D. A., Wang, F., Mohr, D. C., & Choudhury, T. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266516 · Cited by 79 · see citing works
Self-citationA scoping review on monitoring mental health using smart wearable devices
Long, N., Lei, Y., Peng, L., Xu, P., & Mao, P. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022369 · Cited by 72 · see citing works
Chen, S., Yu, J., Chamouni, S., Wang, Y., & Li, Y. (2024) · BMC Medicine · doi:10.1186/s12916-024-03566-x · Cited by 73 · see citing works
“This “black box” nature of some ML models raises concerns about their transparency and accountability, particularly in the context of public health interventions that can have far-reaching consequences [55, 56].”
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Artificial intelligence-assisted psychosis risk screening in adolescents: Practices and challenges
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Exploring Large-Scale Language Models to Evaluate EEG-Based Multimodal Data for Mental Health
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Critical review of self‐diagnosis of mental health conditions using artificial intelligence
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Explainable artificial intelligence systems for predicting mental health problems in autistics
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Review on modeling the societal impact of infrastructure disruptions due to disasters
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Mental Health Analysis of Employees using Machine Learning Techniques
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BackgroundPredictive structured–unstructured interactions in EHR models: A case study of suicide prediction
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“Nevertheless,recentadvancesindeeplearningapproaches show that, particularly for complex high-dimensional datasets such as fMRI data, the deep models show a signifi-cant improvement in performance over standard ML models (Su et al., 2020).”
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“The use of DL algorithms in health and medicine is increasing, and they can diagnose and improve the treatment of patients with MH conditions (Su et al., 2020).”
Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry
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“, clinical data, genetic/genomic data, vocal and visual expression data, and social media data [14].”
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Quantum deep learning in neuroinformatics: a systematic review
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“…(Valliani et al. 2019 ), brain-computer interfacing (Aggarwal and Chugh 2022 ), connectomics (Anbarasi et al. 2024 ), mental health assessments (Su et al. 2020 ), neurofeedback and rehabilitation therapy (Le Franc 2022 ), consciousness and awareness modeling (Lee 2022 ), and neurocognition…”
Deep Learning based techniques for Neuro-degenerative disorders detection
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“14,15 Its capacity to run unattended and extract pertinent characteristics makes it a viable tool for feature extraction in PD. 16 It examines the EEG data and extracts hidden, signi fi cant aspects that successfully capture the underlying illness signs.”
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Anxiety Prediction and Analysis- A Machine Learning Based Approach
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“In an article written by Chang Su [25], that studied the application of deep learning in mental health research, many deep learning techniques were analyzed.”
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“…such as how information is represented in the visual cortex (Cichy & Kaiser, 2019; Dobs et al., 2022; Khaligh-Razavi & Kriegeskorte, 2014; Kohoutová et al., 2020; Konkle & Alvarez, 2022; Mehta et al., 2020; Perconti & Plebe, 2020; Richards et al., 2019; Saxe et al., 2021; Su et al., 2020).”
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A machine learning algorithm for classification of mental tasks
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AI-Assisted Diagnosing, Monitoring and Treatment of Mental Disorders: A Survey
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“Reference [23] presented the recent developments of deep learning methods for mental illness diagnostic and therapeutic purposes.”
A scoping review on deep learning for next-generation RNA-Seq. data analysis
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“As the state-of-the-art wave of AI and ML technologies, deep learning tactics aim to develop a real-time mapping of raw input features directly into output via utilising multi-layer networks and identifying hidden patterns in the large datasets (Su et al. 2020).”
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Self-citationDifferential Privacy Preservation in Robust Continual Learning
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“(2) implies that if we change a tuple in the database slightly, the output distribution does not vary significantly.”
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“Current advances in machine learning (ML) [9] and the widespread adoption of increasingly miniaturized and powerful wearable devices offer the opportunity for personal sensing, which could help mitigate the above problems [10].”
The reanimation of pseudoscience in machine learning and its ethical repercussions
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“…identity, or behavioral features from human likenesses have proposed applications for their methods in surveillance, 8 law enforcement, 7,17,33 the identification of military targets, 22 early prediction of mental illness, 8,16 detection of academic dishonesty, 17,24 and hiring, 17,33 among others.”
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“…using statistical modelling of patients' medical health records to improve the identification of certain mental health problems (most commonly using ‘deep learning’ – a form of artificial intelligence) has been effective in identifying mental health problems (Pham et al., 2017; Su et al., 2020).”
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Advancing mental health care with AI-enabled precision psychiatry tools: A patent review
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Scoping review on natural language processing applications in counselling and psychotherapy
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“For example, NLP has been used to improve patients' psychological well-being by assisting in the diagnostics of mental disorders and personalizing pharmacological treatment (Su et al., 2020).”
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Evaluation of Electronic Health Record-Based Suicide Risk Prediction Models on Contemporary Data
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Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review
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“To the best of our knowledge, previous studies have conducted a scoping review of DL applications in detecting a particular disease, such as Alzheimer’s [64], lung diseases [8] and mental health issues [65].”
ResultJensen, K. H. R., Dam, V. H., Ganz, M., Fisher, P. M., Ip, C.-T., Sankar, A., Marstrand-Joergensen, M. R., Ozenne, B., Osler, M., Penninx, B. W. J. H., et al. (2023) · BMC Psychiatry · doi:10.1186/s12888-023-04618-x · Cited by 16 · see citing works
“However, the number of patients in the cohort (N = 800) represents one of the largest cohorts to date, looking to determine prediction classifiers in MDD [84].”
Background
- Authors
- Su, C., Xu, Z., Pathak, J., & Wang, F.
- Affiliations
- Weill Cornell Medicine (US)
- Corresponding author
- Wang, Fei
- Venue
- Translational Psychiatry
- Published
- 2020-04-22
- Volume
- 10
- Issue
- 1
- Pages
- 116-116
- ISSN
- 2158-3188 (ISSN-L)
- PMC
- PMC7293215
- OpenAlex
- W3034674374
- Cited by
- 368
- References
- 139
- Citations by year
- 2026: 35 · 2025: 83 · 2024: 74 · 2023: 73 · 2022: 60 · 2021: 38 · 2020: 5
- FWCI
- 6.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Translational Psychiatry (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- deep learning, mental health outcomes, depression, artificial intelligence in psychiatry, medical data analysis, social media data, diagnosis and prognosis, clinical decision support
- MeSH terms
- Deep Learning, Algorithms, Artificial Intelligence, Humans, Mental Health, Outcome Assessment, Health Care
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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