Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Expert Review
  • Published:

Applications of artificial intelligence−machine learning for detection of stress: a critical overview

Molecular Psychiatry (2023)Cite this article

Subjects

Abstract

Psychological distress is a major contributor to human physiology and pathophysiology, and it has been linked to several conditions, such as auto-immune diseases, metabolic syndrome, sleep disorders, and suicidal thoughts and inclination. Therefore, early detection and management of chronic stress is crucial for the prevention of several diseases. Artificial intelligence (AI) and Machine Learning (ML) have promoted a paradigm shift in several areas of biomedicine including diagnosis, monitoring, and prognosis of disease. Here, our review aims to present some of the AI and ML applications for solving biomedical issues related to psychological stress. We provide several lines of evidence from previous studies highlighting that AI and ML have been able to predict stress and detect the brain normal states vs. abnormal states (notably, in post-traumatic stress disorder (PTSD)) with accuracy around 90%. Of note, AI/ML-driven technology applied to identify ubiquitously present stress exposure may not reach its full potential, unless future analytics focus on detecting prolonged distress through such technology instead of merely assessing stress exposure. Moving forward, we propose that a new subcategory of AI methods called Swarm Intelligence (SI) can be used towards detecting stress and PTSD. SI involves ensemble learning techniques to efficiently solve a complex problem, such as stress detection, and it offers particular strength in clinical settings, such as privacy preservation. We posit that AI and ML approaches will be beneficial for the medical and patient community when applied to predict and assess stress levels. Last, we encourage additional research to bring AI and ML into the standard clinical practice for diagnostics in the not-too-distant future.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: General flowchart of data classification based on physiological signal.
Fig. 2: An example of ROC curve; the AUC is the measure of performance (based on modified content from ref. [109]).
Fig. 3: Support Vector Machine (SVM) and linear kernel.
Fig. 4: Simple and Deep Neural Networks.

Similar content being viewed by others

References

  1. Gupta R, Alam MA, Agarwal P. Modified support vector machine for detecting stress level using EEG signals. Comput Intell Neurosci. 2020;2020:8860841.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Tan SY, Yip A. Hans Selye (1907–1982): founder of the stress theory. Singap Med J. 2018;59:170.

    Article  Google Scholar 

  3. Chrousos GP, Gold PW. The concepts of stress and stress system disorders. Overview of physical and behavioral homeostasis. JAMA. 1992;267:1244–52.

    Article  CAS  PubMed  Google Scholar 

  4. Chrousos GP. Stress and disorders of the stress system. Nat Rev Endocrinol. 2009;5:374–81.

    Article  CAS  PubMed  Google Scholar 

  5. Smith SM, Vale WW. The role of the hypothalamic-pituitary-adrenal axis in neuroendocrine responses to stress. Dialog Clin Neurosci. 2006;8:383.

    Article  Google Scholar 

  6. Mastorakos G, Magiakou MA, Chrousos GP. Effects of the immune/inflammatory reaction on the hypothalamic-pituitary-adrenal axis. Ann NY Acad Sci. 1995;771:438–48.

    Article  CAS  PubMed  Google Scholar 

  7. Papanicolaou DA, Wilder RL, Manolagas SC, Chrousos GP. The pathophysiologic roles of interleukin-6 in human disease. Ann Intern Med. 1998;128:127–37.

    Article  CAS  PubMed  Google Scholar 

  8. Vgontzas AN, Bixler EO, Lin HM, Prolo P, Trakada G, Chrousos GP. IL-6 and its circadian secretion in humans. Neuroimmunomodulation. 2005;12:131–40.

    Article  CAS  PubMed  Google Scholar 

  9. Koumantarou Malisiova E, Mourikis I, Darviri C, Nicolaides NC, Zervas IM, Papageorgiou C, et al. Hair cortisol concentrations in mental disorders: A systematic review. Physiol Behav. 2021;229:113244.

    Article  CAS  PubMed  Google Scholar 

  10. Bougea A, Anagnostouli M, Angelopoulou E, Spanou I, Chrousos G. Psychosocial and Trauma-Related Stress and Risk of Dementia: A Meta-Analytic Systematic Review of Longitudinal Studies. J Geriatr Psychiatry Neurol. 2022;35:24–37.

  11. Hatzimanolis A, Avramopoulos D, Arking DE, Moes A, Bhatnagar P, Lencz T, et al. Stress-dependent association between polygenic risk for schizophrenia and schizotypal traits in young army recruits. Schizophr Bull. 2018;44:338–47.

    Article  PubMed  Google Scholar 

  12. Mentis AA, Dardiotis E, Efthymiou V, Chrousos GP. Non-genetic risk and protective factors and biomarkers for neurological disorders: a meta-umbrella systematic review of umbrella reviews. BMC Med. 2021;19:6.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Fromer M, Roussos P, Sieberts SK, Johnson JS, Kavanagh DH, Perumal TM, et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci. 2016;19:1442–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hatzimanolis A, Bhatnagar P, Moes A, Wang R, Roussos P, Bitsios P, et al. Common genetic variation and schizophrenia polygenic risk influence neurocognitive performance in young adulthood. Am J Med Genet B Neuropsychiatr Genet. 2015;168b:392–401.

    Article  PubMed  Google Scholar 

  15. Purcell SM, Moran JL, Fromer M, Ruderfer D, Solovieff N, Roussos P, et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature. 2014;506:185–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Roussos P, Giakoumaki SG, Zouraraki C, Fullard JF, Karagiorga VE, Tsapakis EM, et al. The relationship of common risk variants and polygenic risk for schizophrenia to sensorimotor gating. Biol Psychiatry. 2016;79:988–96.

    Article  PubMed  Google Scholar 

  17. Roussos P, Bitsios P, Giakoumaki SG, McClure MM, Hazlett EA, New AS, et al. CACNA1C as a risk factor for schizotypal personality disorder and schizotypy in healthy individuals. Psychiatry Res. 2013;206:122–3.

    Article  CAS  PubMed  Google Scholar 

  18. Roussos P, Giakoumaki SG, Adamaki E, Georgakopoulos A, Robakis NK, Bitsios P. The association of schizophrenia risk D-amino acid oxidase polymorphisms with sensorimotor gating, working memory and personality in healthy males. Neuropsychopharmacology. 2011;36:1677–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Chan K, Lee T-W, Sample PA, Goldbaum MH, Weinreb RN, Sejnowski TJ. Comparison of machine learning and traditional classifiers in glaucoma diagnosis. IEEE Trans Biomed Eng. 2002;49:963–74.

    Article  PubMed  Google Scholar 

  20. Colwell LJ. Statistical and machine learning approaches to predicting protein–ligand interactions. Curr Opin Struct Biol. 2018;49:123–8.

    Article  CAS  PubMed  Google Scholar 

  21. Makridakis S, Spiliotis E, Assimakopoulos V. Statistical and Machine Learning forecasting methods: Concerns and ways forward. PloS one. 2018;13:e0194889.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Chatterjee P, Cymberknop LJ, Armentano RL. Nonlinear systems in healthcare towards intelligent disease prediction. Nonlinear systems—theoretical aspects and recent applications. IntechOpen 2019.

  23. Chrousos GP, Kino T. Intracellular glucocorticoid signaling: a formerly simple system turns stochastic. Science’s STKE. 2005;2005:pe48.

    PubMed  Google Scholar 

  24. Flesia L, Monaro M, Mazza C, Fietta V, Colicino E, Segatto B, et al. Predicting perceived stress related to the Covid-19 outbreak through stable psychological traits and machine learning models. J Clin Med. 2020;9:3350.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. OMURCA, Sevinç İlhan; EKINCI, Ekin. An alternative evaluation of post traumatic stress disorder with machine learning methods. In: Proceedings of the 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA). IEEE, Madrid, Spain, 2015. p. 1–7

  26. Alberdi A, Aztiria A, Basarab A. Towards an automatic early stress recognition system for office environments based on multimodal measurements: a review. J Biomed Inform. 2016;59:49–75.

    Article  PubMed  Google Scholar 

  27. Barua S, Begum S, Ahmed MU. Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals. In: Proceedings of the pHealth. IOS Press BV, Amsterdam, Netherlands, 2015. p. 241–8.

  28. Siegel CE, Laska EM, Lin Z, Xu M, Abu-Amara D, Jeffers MK, et al. Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates. Transl Psychiatry. 2021;11:1–12.

    Article  Google Scholar 

  29. Galatzer-Levy IR, Ma S, Statnikov A, Yehuda R, Shalev AY. Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD. Transl Psychiatry. 2017;7:e1070–e1070.

    Article  CAS  PubMed Central  Google Scholar 

  30. Agorastos A, Chrousos GP. The neuroendocrinology of stress: the stress-related continuum of chronic disease development. Mol Psychiatry. 2022;27:502–13.

    Article  PubMed  Google Scholar 

  31. Love BC. Comparing supervised and unsupervised category learning. Psychonom Bull Rev. 2002;9:829–35.

    Article  Google Scholar 

  32. Camacho DM, Collins KM, Powers RK, Costello JC, Collins JJ. Next-generation machine learning for biological networks. Cell. 2018;173:1581–92.

    Article  CAS  PubMed  Google Scholar 

  33. Goecks J, Jalili V, Heiser LM, Gray JW. How machine learning will transform biomedicine. Cell. 2020;181:92–101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380:1347–58.

    Article  PubMed  Google Scholar 

  35. Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020;395:1579–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Vollmer S, Mateen BA, Bohner G, Király FJ, Ghani R, Jonsson P. et al. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ. 2020;368:6927

    Article  Google Scholar 

  37. Peterson ED. Machine learning, predictive analytics, and clinical practice: can the past inform the present? JAMA. 2019;322:2283–4.

    Article  PubMed  Google Scholar 

  38. Meskó B, Görög M. A short guide for medical professionals in the era of artificial intelligence. npj Digit Med. 2020;3:1–8.

    Article  Google Scholar 

  39. Harrison JH, Gilbertson JR, Hanna MG, Olson NH, Seheult JN, Sorace JM, et al. Introduction to artificial intelligence and machine learning for pathology. Arch Pathol Lab Med. 2021;145:1228–54.

  40. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56.

    Article  CAS  PubMed  Google Scholar 

  41. Bates DW, Auerbach A, Schulam P, Wright A, Saria S. Reporting and implementing interventions involving machine learning and artificial intelligence. Ann Intern Med. 2020;172:S137–S144.

    Article  PubMed  Google Scholar 

  42. Hinton G. Deep learning—a technology with the potential to transform health care. Jama. 2018;320:1101–2.

    Article  PubMed  Google Scholar 

  43. Mentis AA, Garcia I, Jiménez J, Paparoupa M, Xirogianni A, Papandreou A, et al. Artificial intelligence in differential diagnostics of meningitis: a nationwide study. Diagnostics. 2021;11:602.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Richards BA, Lillicrap TP, Beaudoin P, Bengio Y, Bogacz R, Christensen A, et al. A deep learning framework for neuroscience. Nat Neurosci. 2019;22:1761–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Sawalha J, Cao L, Chen J, Selvitella A, Liu Y, Yang C, et al. Individualized identification of first-episode bipolar disorder using machine learning and cognitive tests. J Affect Disord. 2021;282:662–8.

    Article  PubMed  Google Scholar 

  46. Le-Niculescu H, Roseberry K, Levey D, Rogers J, Kosary K, Prabha S, et al. Towards precision medicine for stress disorders: diagnostic biomarkers and targeted drugs. Mol Psychiatry. 2020;25:918–38.

    Article  CAS  PubMed  Google Scholar 

  47. Oquendo M, Baca-Garcia E, Artes-Rodriguez A, Perez-Cruz F, Galfalvy H, Blasco-Fontecilla H, et al. Machine learning and data mining: strategies for hypothesis generation. Mol Psychiatry. 2012;17:956–9.

    Article  CAS  PubMed  Google Scholar 

  48. Passos IC, Mwangi B. Machine learning-guided intervention trials to predict treatment response at an individual patient level: an important second step following randomized clinical trials. Mol Psychiatry. 2020;25:701–2.

    Article  PubMed  Google Scholar 

  49. Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Mol Psychiatry. 2019;24:1583–98.

    Article  PubMed  Google Scholar 

  50. Hedderich DM, Eickhoff SB. Machine learning for psychiatry: getting doctors at the black box? Mol Psychiatry. 2021;26:23–25.

    Article  PubMed  Google Scholar 

  51. Bracher-Smith M, Crawford K, Escott-Price V. Machine learning for genetic prediction of psychiatric disorders: a systematic review. Mol Psychiatry. 2021;26:70–9.

    Article  PubMed  Google Scholar 

  52. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:1–9.

    Article  CAS  Google Scholar 

  53. Comparison of heart rate variability measures for mental stress detection. In: Proceedings of the computing in cardiology. 2011. IEEE.

  54. Mental stress detection using heart rate variability and morphologic variability of EeG signals. In: Proceedings of the international conference and exposition on electrical and power engineering 2012. IEEE.

  55. Remote assessment of the heart rate variability to detect mental stress. In: Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops, 2013. IEEE.

  56. Healey JA, Picard RW. Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans Intell Transport Syst. 2005;6:156–66.

    Article  Google Scholar 

  57. Picard RW, Vyzas E, Healey J. Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans Pattern Anal Mach Intell. 2001;23:1175–91.

    Article  Google Scholar 

  58. Taylor S, Jaques N, Nosakhare E, Sano A, Picard R. Personalized multitask learning for predicting tomorrow’s mood, stress, and health. IEEE Trans Affect Comput. 2017;11:200–13.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Ye C, Kumar BV, Coimbra MT. An automatic subject-adaptable heartbeat classifier based on multiview learning. IEEE J Biomed Health Inf. 2016;20:1485–92.

    Article  Google Scholar 

  60. Huang S-C, Pareek A, Zamanian R, Banerjee I, Lungren MP. Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection. Sci Rep. 2020;10:1–9.

    Article  Google Scholar 

  61. Zheng Y, Wong TC, Leung BH, Poon CC. Unobtrusive and multimodal wearable sensing to quantify anxiety. IEEE Sens J. 2016;16:3689–96.

    Article  Google Scholar 

  62. Classification tree for real-life stress detection using linear Heart Rate Variability analysis. Case study: students under stress due to university examination. In: Proceedings of the World Congress on Medical Physics and Biomedical Engineering May 26–31, 2012, Beijing, China 2013. Springer.

  63. Akella A, Singh AK, Leong D, Lal S, Newton P, Clifton-Bligh R, et al. Classifying multi-level stress responses from brain cortical EEG in nurses and non-health professionals using machine learning auto encoder. IEEE J Transl Eng Health Med. 2021;9:2200109.

    Article  PubMed  Google Scholar 

  64. Li B, Sano A. Extraction and interpretation of deep autoencoder-based temporal features from wearables for forecasting personalized mood, health, and stress. Proc ACM Interact, Mob, Wearable Ubiquitous Technol. 2020;4:1–26.

    Google Scholar 

  65. El Haouij N, Poggi J-M, Ghozi R, Sevestre-Ghalila S, Jaïdane M. Random forest-based approach for physiological functional variable selection for driver’s stress level classification. Stat Methods Appl. 2019;28:157–85.

    Article  Google Scholar 

  66. Tsamardinos I, Charonyktakis P, Papoutsoglou G, Borboudakis G, Lakiotaki K, Zenklusen JC, et al. Just Add Data: automated predictive modeling for knowledge discovery and feature selection. NPJ Precis Oncol. 2022;6:1–17.

    Google Scholar 

  67. Candel A, Parmar V, LeDell E, Arora A. Deep learning with H2O. H2O AI Inc 2016 p. 1–21.

  68. Can YS, Chalabianloo N, Ekiz D, Ersoy C. Continuous stress detection using wearable sensors in real life: algorithmic programming contest case study. Sensors. 2019;19:1849.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Jordan A. On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. Adv Neural Inform Process Syst. 2002;14:841.

    Google Scholar 

  70. Remote measurement of cognitive stress via heart rate variability. In: Proceedings of the 36th annual international conference of the IEEE Engineering in Medicine and Biology Society. 2014. IEEE.

  71. Noble WS. What is a support vector machine? Nat Biotechnol. 2006;24:1565–7.

    Article  CAS  PubMed  Google Scholar 

  72. Scholkopf B, Sung K-K, Burges CJ, Girosi F, Niyogi P, Poggio T, et al. Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans Signal Process. 1997;45:2758–65.

    Article  Google Scholar 

  73. Stress detection in computer users based on digital signal processing of noninvasive physiological variables. In: Proceedings of the international conference of the IEEE engineering in medicine and biology society 2006. IEEE.

  74. Support vector machine for classification of stress subjects using EEG signals. In: Proceedings of the IEEE Conference on Systems, Process and Control (ICSPC 2014) 2014. IEEE.

  75. Attallah O. An effective mental stress state detection and evaluation system using minimum number of frontal brain electrodes. Diagnostics. 2020;10:292.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Subhani AR, Mumtaz W, Saad MNBM, Kamel N, Malik AS. Machine learning framework for the detection of mental stress at multiple levels. IEEE Access. 2017;5:13545–56.

    Article  Google Scholar 

  77. Ho TK. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell. 1998;20:832–44.

    Article  Google Scholar 

  78. Lykken D, Rose R, Luther B, Maley M. Correcting psychophysiological measures for individual differences in range. Psychol Bull. 1966;66:481.

    Article  CAS  PubMed  Google Scholar 

  79. Brodersen KH, Gallusser F, Koehler J, Remy N, Scott SL. Inferring causal impact using Bayesian structural time-series models. Ann Appl Stat. 2015;9:247–74.

    Article  Google Scholar 

  80. Scott SL, Varian HR. Predicting the present with Bayesian structural time series. Int J Math Model Numer Optim. 2014;5:4–23.

    Google Scholar 

  81. Liu J, Spakowicz DJ, Ash GI, Hoyd R, Ahluwalia R, Zhang A, et al. Bayesian structural time series for biomedical sensor data: a flexible modeling framework for evaluating interventions. PLoS Comput Biol. 2021;17:e1009303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Wang S-C. Artificial neural network. Interdisciplinary computing in java programming. Springer 2003, p. 81–100.

  83. Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE. A survey of deep neural network architectures and their applications. Neurocomputing. 2017;234:11–26.

    Article  Google Scholar 

  84. Bolea J, Pueyo E, Orini M, Bailón R. Influence of heart rate in non-linear HRV indices as a sampling rate effect evaluated on supine and standing. Front Physiol. 2016;7:501.

    Article  PubMed  PubMed Central  Google Scholar 

  85. PsychologiCal Stress Detection Using Deep Convolutional Neural Networks. In: Proceedings of the International Conference on Computer Vision and Image Processing 2019. Springer.

  86. Cho Y, Julier SJ, Bianchi-Berthouze N. Instant stress: detection of perceived mental stress through smartphone photoplethysmography and thermal imaging. JMIR Ment Health. 2019;6:e10140.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Can YS, Arnrich B, Ersoy C. Stress detection in daily life scenarios using smart phones and wearable sensors: a survey. J Biomed Inform. 2019;92:103139.

    Article  PubMed  Google Scholar 

  88. Towards mental stress detection using wearable physiological sensors. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2011. IEEE.

  89. Doan S, Yang EW, Tilak SS, Li PW, Zisook DS, Torii M. Extracting health-related causality from Twitter messages using natural language processing. BMC Med Inform Decis Mak. 2019;19:79.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Chekroud AM, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, et al. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry. 2021;20:154–70.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Zaremba W, Sutskever I, Vinyals O. Recurrent neural network regularization. arXiv preprint arXiv:14092329 2014.

  92. Chipman HA, George EI, McCulloch RE. BART: Bayesian additive regression trees. Ann Appl Stat. 2010;4:266–98.

    Article  Google Scholar 

  93. Jamil Z. Monitoring tweets for depression to detect at-risk users. Université d’Ottawa/University of Ottawa 2017.

  94. He Q, Veldkamp BP, Glas CA, de Vries T. Automated assessment of patients’ self-narratives for posttraumatic stress disorder screening using natural language processing and text mining. Assessment. 2017;24:157–72.

    Article  PubMed  Google Scholar 

  95. Cho H-M, Park H, Dong S-Y, Youn I. Ambulatory and laboratory stress detection based on raw electrocardiogram signals using a convolutional neural network. Sensors. 2019;19:4408.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Rodriguez-Paras C, Tippey K, Brown E, Sasangohar F, Creech S, Kum H-C, et al. Posttraumatic stress disorder and mobile health: app investigation and scoping literature review. JMIR mHealth uHealth. 2017;5:e156.

    Article  PubMed  PubMed Central  Google Scholar 

  97. Wshah S, Skalka C, Price M. Predicting posttraumatic stress disorder risk: a machine learning approach. JMIR Ment Health. 2019;6:e13946.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Gini C. Concentration and dependency ratios. Riv Polit Econom. 1997;87:769–92.

    Google Scholar 

  99. Saxe GN, Ma S, Ren J, Aliferis C. Machine learning methods to predict child posttraumatic stress: a proof of concept study. BMC Psychiatry. 2017;17:1–13.

    Article  Google Scholar 

  100. Karstoft K-I, Galatzer-Levy IR, Statnikov A, Li Z, Shalev AY. Bridging a translational gap: using machine learning to improve the prediction of PTSD. BMC Psychiatry. 2015;15:1–7.

    Article  Google Scholar 

  101. Galatzer-Levy IR, Karstoft K-I, Statnikov A, Shalev AY. Quantitative forecasting of PTSD from early trauma responses: a machine learning application. J Psychiatr Res. 2014;59:68–76.

    Article  PubMed  PubMed Central  Google Scholar 

  102. Galatzer-Levy IR, Bonanno GA. Optimism and death: Predicting the course and consequences of depression trajectories in response to heart attack. Psychol Sci. 2014;25:2177–88.

    Article  PubMed  Google Scholar 

  103. Galatzer-Levy IR, Bonanno GA, Bush DE, LeDoux J. Heterogeneity in threat extinction learning: Substantive and methodological considerations for identifying individual difference in response to stress. Front Behav Neurosci. 2013;7:55.

    Article  PubMed  PubMed Central  Google Scholar 

  104. Galatzer-Levy IR, Bryant RA. 636,120 ways to have posttraumatic stress disorder. Perspect Psychol Sci. 2013;8:651–62.

    Article  PubMed  Google Scholar 

  105. Galatzer-Levy IR, Ruggles KV, Chen Z. Data science in the Research Domain Criteria era: relevance of machine learning to the study of stress pathology, recovery, and resilience. Chronic Stress. 2018;2:2470547017747553.

    Article  PubMed  PubMed Central  Google Scholar 

  106. Galatzer-Levy IR, Steenkamp MM, Brown AD, Qian M, Inslicht S, Henn-Haase C, et al. Cortisol response to an experimental stress paradigm prospectively predicts long-term distress and resilience trajectories in response to active police service. J Psychiatr Res. 2014;56:36–42.

    Article  PubMed  PubMed Central  Google Scholar 

  107. Karstoft K-I, Statnikov A, Andersen SB, Madsen T, Galatzer-Levy IR. Early identification of posttraumatic stress following military deployment: application of machine learning methods to a prospective study of Danish soldiers. J Affect Disord. 2015;184:170–5.

    Article  PubMed  Google Scholar 

  108. Schultebraucks K, Qian M, Abu-Amara D, Dean K, Laska E, Siegel C, et al. Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors. Mol Psychiatry. 2020;26:1–12.

    Google Scholar 

  109. McDonald AD, Sasangohar F, Jatav A, Rao AH. Continuous monitoring and detection of post-traumatic stress disorder (PTSD) triggers among veterans: a supervised machine learning approach. IISE Trans Healthc Syst Eng. 2019;9:201–11.

    Article  Google Scholar 

  110. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc: Ser B (Methodol). 1995;57:289–300.

    Google Scholar 

  111. Geronikolou S, Drosatos G, Chrousos G. Emotional analysis of twitter posts during the first phase of the COVID-19 pandemic in Greece: infoveillance study. JMIR Form Res. 2021;5:e27741.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Abd Rahman R, Omar K, Noah SAM, Danuri MSNM, Al-Garadi MA. Application of machine learning methods in mental health detection: a systematic review. IEEE Access. 2020;8:183952–64.

    Article  Google Scholar 

  113. Pries L-K, van Os J, Ten Have M, de Graaf R, van Dorsselaer S, Bak M, et al. Association of recent stressful life events with mental and physical health in the context of genomic and exposomic liability for schizophrenia. JAMA Psychiatry. 2020;77:1296–304.

    Article  PubMed  Google Scholar 

  114. Galatzer-Levy IR, Huang SH, Bonanno GA. Trajectories of resilience and dysfunction following potential trauma: a review and statistical evaluation. Clin Psychol Rev. 2018;63:41–55.

    Article  PubMed  Google Scholar 

  115. Norris FH, Tracy M, Galea S. Looking for resilience: understanding the longitudinal trajectories of responses to stress. Soc Sci Med. 2009;68:2190–8.

    Article  PubMed  Google Scholar 

  116. Schultebraucks K, Shalev AY, Michopoulos V, Grudzen CR, Shin SM, Stevens JS, et al. A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor. Nat Med. 2020;26:1084–8.

    Article  CAS  PubMed  Google Scholar 

  117. Schultebraucks K, Sijbrandij M, Galatzer-Levy I, Mouthaan J, Olff M, van Zuiden M. Forecasting individual risk for long-term Posttraumatic Stress Disorder in emergency medical settings using biomedical data: a machine learning multicenter cohort study. Neurobiol Stress. 2021;14:100297.

    Article  PubMed  PubMed Central  Google Scholar 

  118. Schultebraucks K, Ben-Zion Z, Admon R, Keynan JN, Liberzon I, Hendler T, et al. Assessment of early neurocognitive functioning increases the accuracy of predicting chronic PTSD risk. Mol Psychiatry. 2022;27:2247–54.

    Article  CAS  PubMed  Google Scholar 

  119. Straus LD, An X, Ji Y, McLean SA, Neylan TC, Cakmak AS, et al. Utility of wrist-wearable data for assessing pain, sleep, and anxiety outcomes after traumatic stress exposure. JAMA Psychiatry. 2023.

  120. Beaudoin FL, An X, Basu A, Ji Y, Liu M, Kessler RC, et al. Use of serial smartphone-based assessments to characterize diverse neuropsychiatric symptom trajectories in a large trauma survivor cohort. Transl Psychiatry. 2023;13:4.

    Article  PubMed  PubMed Central  Google Scholar 

  121. Swarm intelligence in cellular robotic systems. In: Proceedings of the Robots and biological systems: towards a new bionics? 1993. Springer.

  122. Grosan C, Abraham A, Chis M. Swarm intelligence in data mining. Springer 2006.

  123. Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, et al. Swarm Learning as a privacy-preserving machine learning approach for disease classification. BioRxiv. 2020. 2020.06. 25.171009.

  124. Particle swarm optimization. In: Proceedings of the Proceedings of ICNN'95-international conference on neural networks 1995. IEEE.

  125. Bonabeau E, Corne D, Poli R. Swarm intelligence: the state of the art special issue of natural computing. Nat Comput. 2010;9:655–7.

    Article  Google Scholar 

  126. An ensemble PSO-based approach for diagnosis of coronary artery disease. In: Proceedings of the International Symposium on Artificial Intelligence and Signal Processing (AISP). 2011. IEEE.

  127. Best MG, Sol N, GJG S, Vancura A, Muller M, Niemeijer A-LN, et al. Swarm intelligence-enhanced detection of non-small-cell lung cancer using tumor-educated platelets. Cancer Cell. 2017;32:238–52.e239.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Chuang L-Y, Lin Y-D, Chang H-W, Yang C-H. An improved PSO algorithm for generating protective SNP barcodes in breast cancer. PLoS One. 2012;7:e37018.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Ludermir TB, De Oliveira WR. Particle swarm optimization of MLP for the identification of factors related to common mental disorders. Expert Syst Appl. 2013;40:4648–52.

    Article  Google Scholar 

  130. Feature selection for bi-objective stress classification using emerging swarm intelligence metaheuristic techniques. In: Proceedings of the Proceedings of Data Analytics and Management: ICDAM. 2021, Volume 2, 2022. Springer.

  131. Sharma S, Singh G, Sharma M. A comprehensive review and analysis of supervised-learning and soft computing techniques for stress diagnosis in humans. Comput Biol Med. 2021;134:104450.

    Article  PubMed  Google Scholar 

  132. de Santos Sierra A, Ávila CS, Casanova JG, del Pozo GB. A stress-detection system based on physiological signals and fuzzy logic. IEEE Trans Ind Electron. 2011;58:4857–65.

    Article  Google Scholar 

  133. Stress detection from audio on multiple window analysis size in a public speaking task. In: Proceedings of the Humaine Association Conference on Affective Computing and Intelligent Interaction. 2013. IEEE.

  134. Vanitha V, Krishnan P. Real-time stress detection system based on EEG signals. 2016.

  135. Mozos OM, Sandulescu V, Andrews S, Ellis D, Bellotto N, Dobrescu R, et al. Stress detection using wearable physiological and sociometric sensors. Int J Neural Syst. 2017;27:1650041.

    Article  PubMed  Google Scholar 

  136. Understanding physiological responses to stressors during physical activity. In: Proceedings of the ACM conference on ubiquitous computing. 2012.

  137. Akmandor AO, Jha NK. Keep the stress away with SoDA: Stress detection and alleviation system. IEEE Trans Multi-Scale Comput Syst. 2017;3:269–82.

    Article  Google Scholar 

  138. Asif A, Majid M, Anwar SM. Human stress classification using EEG signals in response to music tracks. Comput Biol Med. 2019;107:182–96.

    Article  PubMed  Google Scholar 

  139. Jin C, Jia H, Lanka P, Rangaprakash D, Li L, Liu T, et al. Dynamic brain connectivity is a better predictor of PTSD than static connectivity. Hum Brain Mapp. 2017;38:4479–96.

    Article  PubMed  PubMed Central  Google Scholar 

  140. Kessler RC, Rose S, Koenen KC, Karam EG, Stang PE, Stein DJ, et al. How well can post‐traumatic stress disorder be predicted from pre‐trauma risk factors? An exploratory study in the WHO World Mental Health Surveys. World Psychiatry. 2014;13:265–74.

    Article  PubMed  PubMed Central  Google Scholar 

  141. Liu F, Xie B, Wang Y, Guo W, Fouche J-P, Long Z, et al. Characterization of post-traumatic stress disorder using resting-state fMRI with a multi-level parametric classification approach. Brain Topogr. 2015;28:221–37.

    Article  PubMed  Google Scholar 

  142. Reece AG, Danforth CM. Instagram photos reveal predictive markers of depression. EPJ Data Sci. 2017;6:1–12.

    Google Scholar 

  143. Rosellini AJ, Dussaillant F, Zubizarreta JR, Kessler RC, Rose S. Predicting posttraumatic stress disorder following a natural disaster. J Psychiatr Res. 2018;96:15–22.

    Article  PubMed  Google Scholar 

  144. Tahmasian M, Jamalabadi H, Abedini M, Ghadami MR, Sepehry AA, Knight DC, et al. Differentiation chronic post traumatic stress disorder patients from healthy subjects using objective and subjective sleep-related parameters. Neurosci Lett. 2017;650:174–9.

    Article  CAS  PubMed  Google Scholar 

  145. Tylee DS, Chandler SD, Nievergelt CM, Liu X, Pazol J, Woelk CH, et al. Blood-based gene-expression biomarkers of post-traumatic stress disorder among deployed marines: a pilot study. Psychoneuroendocrinology. 2015;51:472–94.

    Article  CAS  PubMed  Google Scholar 

  146. The relationship between Precision-Recall and ROC curves. In: Proceedings of the 23rd international conference on Machine learning. 2006.

  147. Area under the precision-recall curve: point estimates and confidence intervals. In: Proceedings of the Joint European conference on machine learning and knowledge discovery in databases. 2013. Springer.

  148. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the Ijcai. 1995. Montreal, Canada.

Download references

Author information

Authors and Affiliations

  1. University Research Institute of Maternal and Child Health & Precision Medicine, Athens, Greece

    Alexios-Fotios A. Mentis

  2. UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, “Aghia Sophia” Children’s Hospital, Athens, Greece

    Alexios-Fotios A. Mentis

  3. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    Donghoon Lee & Panos Roussos

  4. Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    Donghoon Lee & Panos Roussos

  5. Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    Donghoon Lee & Panos Roussos

  6. Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA

    Panos Roussos

Authors
  1. Alexios-Fotios A. Mentis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Donghoon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Panos Roussos
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A-FAM, DL, PR: Conceived the study; A-FAM: Drafted the initial version of the manuscript with input from DL and PR; DL, PR: Critically revised and extended the manuscript for major intellectual content; A-FAM, DL, PR: Read and approved the final version of the manuscript.

Corresponding author

Correspondence to Alexios-Fotios A. Mentis.

Ethics declarations

Competing interests

A-FAM serves as Editorial Board Member of “Translational Psychiatry”, “Systematic Reviews”, and “Annals of Epidemiology”. There are no other financial or personal conflicts of interest to be reported.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Review’s Search Strategy

This narrative review was based on an extensive search in PubMed and Scopus electronic databases until May 2021 for applications of ML methods and other sub-categories of AI to detect stress and PTSD. Our initial search ended in a vast number of studies; thus, further investigation was essential to maintain the most relevant articles. To filter the studies, we focused on articles with subject keywords related to applications of NLP and AI in detecting stress and PTSD. Examples of search terms are “PTSD AND ML”, “PTSD AND classification”, “prediction AND stress”, “stress AND ML”. Those studies that assessed stress and PTSD based on statistical, and not AI/ML methods with clinical point of view, were excluded. Since ML methods utilize various types of input data, all studies with any data type (image, signal, demographic, numerical, and so on) were included. Based on our initial search, it became obvious there was a rapid, if not exponential, increase in publications focused on ML and NLP for detecting mental diseases; therefore, it was not possible to consider all the current related papers in this study. However, we attempted to include major publications in order to show the ability of ML and NLP in detecting stress and disorders related to stress, such as PTSD. Most of the references were from published papers within the last five years. A major effort was placed on presenting important aspects of the studies such as the number of patients, types of features, ML method, and the achieved accuracy for each proposed method. Last, additional papers were added during the peer-review process following the kind suggestion of the anonymous reviewers.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mentis, AF.A., Lee, D. & Roussos, P. Applications of artificial intelligence−machine learning for detection of stress: a critical overview. Mol Psychiatry (2023). https://doi.org/10.1038/s41380-023-02047-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41380-023-02047-6

Search

Advanced search

Quick links