Gupta R, Alam MA, Agarwal P. Modified support vector machine for detecting stress level using EEG signals. Comput Intell Neurosci. 2020;2020:8860841.
Tan SY, Yip A. Hans Selye (1907–1982): founder of the stress theory. Singap Med J. 2018;59:170.
Chrousos GP, Gold PW. The concepts of stress and stress system disorders. Overview of physical and behavioral homeostasis. JAMA. 1992;267:1244–52.
Chrousos GP. Stress and disorders of the stress system. Nat Rev Endocrinol. 2009;5:374–81.
Smith SM, Vale WW. The role of the hypothalamic-pituitary-adrenal axis in neuroendocrine responses to stress. Dialog Clin Neurosci. 2006;8:383.
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.
Papanicolaou DA, Wilder RL, Manolagas SC, Chrousos GP. The pathophysiologic roles of interleukin-6 in human disease. Ann Intern Med. 1998;128:127–37.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Colwell LJ. Statistical and machine learning approaches to predicting protein–ligand interactions. Curr Opin Struct Biol. 2018;49:123–8.
Makridakis S, Spiliotis E, Assimakopoulos V. Statistical and Machine Learning forecasting methods: Concerns and ways forward. PloS one. 2018;13:e0194889.
Chatterjee P, Cymberknop LJ, Armentano RL. Nonlinear systems in healthcare towards intelligent disease prediction. Nonlinear systems—theoretical aspects and recent applications. IntechOpen 2019.
Chrousos GP, Kino T. Intracellular glucocorticoid signaling: a formerly simple system turns stochastic. Science’s STKE. 2005;2005:pe48.
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.
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
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.
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.
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.
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.
Agorastos A, Chrousos GP. The neuroendocrinology of stress: the stress-related continuum of chronic disease development. Mol Psychiatry. 2022;27:502–13.
Love BC. Comparing supervised and unsupervised category learning. Psychonom Bull Rev. 2002;9:829–35.
Camacho DM, Collins KM, Powers RK, Costello JC, Collins JJ. Next-generation machine learning for biological networks. Cell. 2018;173:1581–92.
Goecks J, Jalili V, Heiser LM, Gray JW. How machine learning will transform biomedicine. Cell. 2020;181:92–101.
Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380:1347–58.
Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020;395:1579–86.
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
Peterson ED. Machine learning, predictive analytics, and clinical practice: can the past inform the present? JAMA. 2019;322:2283–4.
Meskó B, Görög M. A short guide for medical professionals in the era of artificial intelligence. npj Digit Med. 2020;3:1–8.
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.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56.
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.
Hinton G. Deep learning—a technology with the potential to transform health care. Jama. 2018;320:1101–2.
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.
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.
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.
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.
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.
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.
Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Mol Psychiatry. 2019;24:1583–98.
Hedderich DM, Eickhoff SB. Machine learning for psychiatry: getting doctors at the black box? Mol Psychiatry. 2021;26:23–25.
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.
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.
Comparison of heart rate variability measures for mental stress detection. In: Proceedings of the computing in cardiology. 2011. IEEE.
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.
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.
Healey JA, Picard RW. Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans Intell Transport Syst. 2005;6:156–66.
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.
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.
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.
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.
Zheng Y, Wong TC, Leung BH, Poon CC. Unobtrusive and multimodal wearable sensing to quantify anxiety. IEEE Sens J. 2016;16:3689–96.
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.
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.
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.
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.
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.
Candel A, Parmar V, LeDell E, Arora A. Deep learning with H2O. H2O AI Inc 2016 p. 1–21.
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.
Jordan A. On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. Adv Neural Inform Process Syst. 2002;14:841.
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.
Noble WS. What is a support vector machine? Nat Biotechnol. 2006;24:1565–7.
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.
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.
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.
Attallah O. An effective mental stress state detection and evaluation system using minimum number of frontal brain electrodes. Diagnostics. 2020;10:292.
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.
Ho TK. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell. 1998;20:832–44.
Lykken D, Rose R, Luther B, Maley M. Correcting psychophysiological measures for individual differences in range. Psychol Bull. 1966;66:481.
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.
Scott SL, Varian HR. Predicting the present with Bayesian structural time series. Int J Math Model Numer Optim. 2014;5:4–23.
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.
Wang S-C. Artificial neural network. Interdisciplinary computing in java programming. Springer 2003, p. 81–100.
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.
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.
PsychologiCal Stress Detection Using Deep Convolutional Neural Networks. In: Proceedings of the International Conference on Computer Vision and Image Processing 2019. Springer.
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.
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.
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.
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.
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.
Zaremba W, Sutskever I, Vinyals O. Recurrent neural network regularization. arXiv preprint arXiv:14092329 2014.
Chipman HA, George EI, McCulloch RE. BART: Bayesian additive regression trees. Ann Appl Stat. 2010;4:266–98.
Jamil Z. Monitoring tweets for depression to detect at-risk users. Université d’Ottawa/University of Ottawa 2017.
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.
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.
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.
Wshah S, Skalka C, Price M. Predicting posttraumatic stress disorder risk: a machine learning approach. JMIR Ment Health. 2019;6:e13946.
Gini C. Concentration and dependency ratios. Riv Polit Econom. 1997;87:769–92.
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.
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.
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.
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.
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.
Galatzer-Levy IR, Bryant RA. 636,120 ways to have posttraumatic stress disorder. Perspect Psychol Sci. 2013;8:651–62.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Norris FH, Tracy M, Galea S. Looking for resilience: understanding the longitudinal trajectories of responses to stress. Soc Sci Med. 2009;68:2190–8.
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.
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.
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.
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.
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.
Swarm intelligence in cellular robotic systems. In: Proceedings of the Robots and biological systems: towards a new bionics? 1993. Springer.
Grosan C, Abraham A, Chis M. Swarm intelligence in data mining. Springer 2006.
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.
Particle swarm optimization. In: Proceedings of the Proceedings of ICNN’95–international conference on neural networks 1995. IEEE.
Bonabeau E, Corne D, Poli R. Swarm intelligence: the state of the art special issue of natural computing. Nat Comput. 2010;9:655–7.
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.
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.
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.
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.
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.
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.
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.
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.
Vanitha V, Krishnan P. Real-time stress detection system based on EEG signals. 2016.
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.
Understanding physiological responses to stressors during physical activity. In: Proceedings of the ACM conference on ubiquitous computing. 2012.
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.
Asif A, Majid M, Anwar SM. Human stress classification using EEG signals in response to music tracks. Comput Biol Med. 2019;107:182–96.
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.
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.
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.
Reece AG, Danforth CM. Instagram photos reveal predictive markers of depression. EPJ Data Sci. 2017;6:1–12.
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.
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.
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.
The relationship between Precision-Recall and ROC curves. In: Proceedings of the 23rd international conference on Machine learning. 2006.
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.
A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the Ijcai. 1995. Montreal, Canada.