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                
Tan SY, Yip A. Hans Selye (19071982): founder of the stress theory. Singap Med J. 2018;59:170.
Article                        Google Scholar                
Chrousos GP, Gold PW. The concepts of stress and stress system disorders. Overview of physical and behavioral homeostasis. JAMA. 1992;267:124452.
Article    CAS    PubMed                        Google Scholar                
Chrousos GP. Stress and disorders of the stress system. Nat Rev Endocrinol. 2009;5:37481.
Article    CAS    PubMed                        Google Scholar                
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                
Mastorakos G, Magiakou MA, Chrousos GP. Effects of the immune/inflammatory reaction on the hypothalamic-pituitary-adrenal axis. Ann NY Acad Sci. 1995;771:43848.
Article    CAS    PubMed                        Google Scholar                
Papanicolaou DA, Wilder RL, Manolagas SC, Chrousos GP. The pathophysiologic roles of interleukin-6 in human disease. Ann Intern Med. 1998;128:12737.
Article    CAS    PubMed                        Google Scholar                
Vgontzas AN, Bixler EO, Lin HM, Prolo P, Trakada G, Chrousos GP. IL-6 and its circadian secretion in humans. Neuroimmunomodulation. 2005;12:13140.
Article    CAS    PubMed                        Google Scholar                
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                
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;35:2437.
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;44:33847.
Article    PubMed                        Google Scholar                
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                
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;19:144253.
Article    CAS    PubMed    PubMed Central                        Google Scholar                
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:392401.
Article    PubMed                        Google Scholar                
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;506:18590.
Article    CAS    PubMed    PubMed Central                        Google Scholar                
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;79:98896.
Article    PubMed                        Google Scholar                
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;206:1223.
Article    CAS    PubMed                        Google Scholar                
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;36:167788.
Article    CAS    PubMed    PubMed Central                        Google Scholar                
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;49:96374.
Article    PubMed                        Google Scholar                
Colwell LJ. Statistical and machine learning approaches to predicting proteinligand interactions. Curr Opin Struct Biol. 2018;49:1238.
Article    CAS    PubMed                        Google Scholar                
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                
Chatterjee P, Cymberknop LJ, Armentano RL. Nonlinear systems in healthcare towards intelligent disease prediction. Nonlinear systemstheoretical aspects and recent applications. IntechOpen 2019.
Chrousos GP, Kino T. Intracellular glucocorticoid signaling: a formerly simple system turns stochastic. Sciences STKE. 2005;2005:pe48.
PubMed                        Google Scholar                
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                
OMURCA, Sevin lhan; 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. 17
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;59:4975.
Article    PubMed                        Google Scholar                
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. 2418.
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;11:112.
Article                        Google Scholar                
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:e1070e1070.
Article    CAS    PubMed Central                        Google Scholar                
Agorastos A, Chrousos GP. The neuroendocrinology of stress: the stress-related continuum of chronic disease development. Mol Psychiatry. 2022;27:50213.
Article    PubMed                        Google Scholar                
Love BC. Comparing supervised and unsupervised category learning. Psychonom Bull Rev. 2002;9:82935.
Article                        Google Scholar                
Camacho DM, Collins KM, Powers RK, Costello JC, Collins JJ. Next-generation machine learning for biological networks. Cell. 2018;173:158192.
Article    CAS    PubMed                        Google Scholar                
Goecks J, Jalili V, Heiser LM, Gray JW. How machine learning will transform biomedicine. Cell. 2020;181:92101.
Article    CAS    PubMed    PubMed Central                        Google Scholar                
Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380:134758.
Article    PubMed                        Google Scholar                
Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020;395:157986.
Article    CAS    PubMed    PubMed Central                        Google Scholar                
Vollmer S, Mateen BA, Bohner G, Kirly 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                
Peterson ED. Machine learning, predictive analytics, and clinical practice: can the past inform the present? JAMA. 2019;322:22834.
Article    PubMed                        Google Scholar                
Mesk B, Grg M. A short guide for medical professionals in the era of artificial intelligence. npj Digit Med. 2020;3:18.
Article                        Google Scholar                
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;145:122854.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:4456.
Article    CAS    PubMed                        Google Scholar                
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:S137S144.
Article    PubMed                        Google Scholar                
Hinton G. Deep learninga technology with the potential to transform health care. Jama. 2018;320:11012.
Article    PubMed                        Google Scholar                
Mentis AA, Garcia I, Jimnez 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                
Richards BA, Lillicrap TP, Beaudoin P, Bengio Y, Bogacz R, Christensen A, et al. A deep learning framework for neuroscience. Nat Neurosci. 2019;22:176170.
Article    CAS    PubMed    PubMed Central                        Google Scholar                
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;282:6628.
Article    PubMed                        Google Scholar                
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;25:91838.
Article    CAS    PubMed                        Google Scholar                
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;17:9569.
Article    CAS    PubMed                        Google Scholar                
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;25:7012.
Article    PubMed                        Google Scholar                
Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Mol Psychiatry. 2019;24:158398.
Article    PubMed                        Google Scholar                
Hedderich DM, Eickhoff SB. Machine learning for psychiatry: getting doctors at the black box? Mol Psychiatry. 2021;26:2325.
Article    PubMed                        Google Scholar                
Bracher-Smith M, Crawford K, Escott-Price V. Machine learning for genetic prediction of psychiatric disorders: a systematic review. Mol Psychiatry. 2021;26:709.
Article    PubMed                        Google Scholar                
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:19.
Article    CAS                        Google Scholar                
Comparison of heart rate variability measures for mental stress detection. In: Proceedings of the computing in cardiology. 2011. IEEE.
Read more:
Applications of artificial intelligencemachine learning for detection ... - Nature.com
Read More..