Gupta R, Alam MA, Agarwal P. Modified support vector machine for detecting stress level using EEG signals. Comput Intell Neurosci. 2020;2020:8860841.
Google Scholar
Tan SY, Yip A. Hans Selye (1907–1982): founder of the stress theory. Singap Med J. 2018;59:170.
Google Scholar
Chrousos GP, Gold PW. The concepts of stress and stress system disorders. Overview of physical and behavioral homeostasis. JAMA. 1992;267:1244–52.
Google Scholar
Chrousos GP. Stress and disorders of the stress system. Nat Rev Endocrinol. 2009;5:374–81.
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.
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;771:438–48.
Google Scholar
Papanicolaou DA, Wilder RL, Manolagas SC, Chrousos GP. The pathophysiologic roles of interleukin-6 in human disease. Ann Intern Med. 1998;128:127–37.
Google Scholar
Vgontzas AN, Bixler EO, Lin HM, Prolo P, Trakada G, Chrousos GP. IL-6 and its circadian secretion in humans. Neuroimmunomodulation. 2005;12:131–40.
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.
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;35:24–37.
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.
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.
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;19:1442–53.
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:392–401.
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;506:185–90.
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;79:988–96.
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;206:122–3.
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;36:1677–88.
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;49:963–74.
Google Scholar
Colwell LJ. Statistical and machine learning approaches to predicting protein–ligand interactions. Curr Opin Struct Biol. 2018;49:123–8.
Google Scholar
Makridakis S, Spiliotis E, Assimakopoulos V. Statistical and Machine Learning forecasting methods: Concerns and ways forward. PloS one. 2018;13:e0194889.
Google Scholar
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.
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.
Google Scholar
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.
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. 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.
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:e1070–e1070.
Google Scholar
Agorastos A, Chrousos GP. The neuroendocrinology of stress: the stress-related continuum of chronic disease development. Mol Psychiatry. 2022;27:502–13.
Google Scholar
Love BC. Comparing supervised and unsupervised category learning. Psychonom Bull Rev. 2002;9:829–35.
Google Scholar
Camacho DM, Collins KM, Powers RK, Costello JC, Collins JJ. Next-generation machine learning for biological networks. Cell. 2018;173:1581–92.
Google Scholar
Goecks J, Jalili V, Heiser LM, Gray JW. How machine learning will transform biomedicine. Cell. 2020;181:92–101.
Google Scholar
Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380:1347–58.
Google Scholar
Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020;395:1579–86.
Google Scholar
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
Google Scholar
Peterson ED. Machine learning, predictive analytics, and clinical practice: can the past inform the present? JAMA. 2019;322:2283–4.
Google Scholar
Meskó B, Görög M. A short guide for medical professionals in the era of artificial intelligence. npj Digit Med. 2020;3:1–8.
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;145:1228–54.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56.
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:S137–S144.
Google Scholar
Hinton G. Deep learning—a technology with the potential to transform health care. Jama. 2018;320:1101–2.
Google Scholar
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.
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;22:1761–70.
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;282:662–8.
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;25:918–38.
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;17:956–9.
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;25:701–2.
Google Scholar
Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Mol Psychiatry. 2019;24:1583–98.
Google Scholar
Hedderich DM, Eickhoff SB. Machine learning for psychiatry: getting doctors at the black box? Mol Psychiatry. 2021;26:23–25.
Google Scholar
Bracher-Smith M, Crawford K, Escott-Price V. Machine learning for genetic prediction of psychiatric disorders: a systematic review. Mol Psychiatry. 2021;26:70–9.
Google Scholar
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:1–9.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
Zheng Y, Wong TC, Leung BH, Poon CC. Unobtrusive and multimodal wearable sensing to quantify anxiety. IEEE Sens J. 2016;16:3689–96.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
Ho TK. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell. 1998;20:832–44.
Google Scholar
Lykken D, Rose R, Luther B, Maley M. Correcting psychophysiological measures for individual differences in range. Psychol Bull. 1966;66:481.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
Wshah S, Skalka C, Price M. Predicting posttraumatic stress disorder risk: a machine learning approach. JMIR Ment Health. 2019;6:e13946.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
Galatzer-Levy IR, Bryant RA. 636,120 ways to have posttraumatic stress disorder. Perspect Psychol Sci. 2013;8:651–62.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
Norris FH, Tracy M, Galea S. Looking for resilience: understanding the longitudinal trajectories of responses to stress. Soc Sci Med. 2009;68:2190–8.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
Asif A, Majid M, Anwar SM. Human stress classification using EEG signals in response to music tracks. Comput Biol Med. 2019;107:182–96.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.