Biomedical Models

Biomedical Models, Algorithms, and Integrated Quantitative Frameworks


[20] Valeriy Gavrishchaka, Olga Senyukova and Mark Koepke, “Synergy of physics-based reasoning and machine learning in biomedical applications: towards unlimited deep learning with limited data”, Advances in Physics: X (2019)

[19] Rebecca Miao, Zhenyi Yang and Valeriy Gavrishchaka, “Leveraging Domain-expert Knowledge, Boosting and Deep Learning for Identification of Rare and Complex States”, Journal of Physics: Conference Series (2019) (3rd International Conference on Control Engineering and Artificial Intelligence)

[18] Valeriy Gavrishchaka, Zhenyi Yang, Rebecca Miao and Olga Senyukova, “Advantages of hybrid deep learning frameworks in applications with limited data”, International Journal of Machine Learning and Computing (2018)

[17] O. Senyukova, V. Gavrishchaka and K. Tulnova, “Multi-Expert Evolving System for Objective Psychophysiological Monitoring and Fast Discovery of Effective Personalized Therapies”, 2017 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS 2017)

[16] O. Senyukova, V. Gavrishchaka, K. Tulnova and A. Monin, “Automated System for Psychophysiological Monitoring and Discovery of Personalized Therapies”, Medical Psychology in Russia, 6 (47), 2017

[15] O. Senyukova, V. Gavrishchaka, M. Sasonko, Y. Gurfinkel, S. Gorokhova, and N. Antsygin, “Generic Ensemble-Based Representation of Global Cardiovascular Dynamics for Personalized Treatment Discovery and Optimization”, Computational Collective Intelligence: 8th International Conference, ICCCI 2016, Halkidiki, Greece, September 28-30, 2016. Proceedings, Part I. � Vol. 9875 of Lecture Notes in Computer Science. � Springer International Publishing Cham, Switzerland, 2016. � P. 197�207

[14] O. Senyukova and V. Gavrishchaka, “Generic Multi-Complexity Representation of Cardiodynamics: From Early Detection of Emerging Abnormalities to Personalized Treatment Optimization”, BIT’s 7th Annual International Congress of Cardiology-2015 (Shanghai, China). � 2015. � P. 176

[13] V.V. Gavrishchaka, O. Senyukova, and K. Davis, “Multi-Complexity Ensemble Measures for Gait Time Series Analysis: Application to Diagnostics, Monitoring and Biometrics”, Book chapter in “Signal and Image Analysis for Biomedical and Life Sciences”, Springer, 2015

[12] O. Senyukova, V. Gavrishchaka, and M. Koepke, “Universal multi-complexity measures for physiological state quantification in intelligent diagnostics and monitoring systems”, Communications in Computer and Information Science, Springer, 2013

[11] V.V. Gavrishchaka, K. Davis, and O. Senyukova, “Multi-complexity measures for early detection and monitoring of neurological abnormalities from gait time series”, in AIP proceedings of 2013 International Symposium on Computational Models for Life Sciences (CMLS-13), Sydney, Australia, 2013

[10] V.V. Gavrishchaka and O.V. Senyukova, “Robust algorithmic detection of cardiac pathologies from short periods of RR data”, Book chapter in “Knowledge-Based Systems in Biomedicine and Computational Life Science”, Springer, 2013

[9] O.V. Senyukova, “Development of machine learning algorithms for semantic segmentation and classification of low-dimensional biomedical signals”, PhD Thesis, Moscow State University, Russian Federation, 2012 (in Russian)

[8] V.V. Gavrishchaka, O.V. Senyukova, M.E. Koepke, and A.I. Kryuchkova, “Multi-objective physiological indicators based on complementary complexity measures: application to early diagnostics and prediction of acute events”, In: International Conference on Computer and Computational Intelligence. Bangkok, Thailand; 2011. p. 95-106

[7] V.V. Gavrishchaka, O.V. Senyukova, O.N. Ulyanova, and A.G. Monin, “Physiological meta-indicators for professional sports applications: express diagnostics, overtraining detection, and quantification of individual zones of optimal functioning”, In: VII International scientific and practical conference for memory of P.Roudik.; 2011. p. 5-7

[6] O.V. Senyukova, V.V. Gavrishchaka, and Y.M. Bayakovskiy, “Methods of automated HRV-based diagnostics”, submitted (in Russian)

[5] O.V. Senyukova and V.V. Gavrishchaka, “Ensemble Decomposition Learning for Optimal Utilization of Implicitly Encoded Knowledge in Biomedical Applications”, In: IASTED International Conference on Computational Intelligence and Bioinformatics. Pittsburgh, USA; 2011. p. 69-73

[4] O.V. Senyukova and V.V. Gavrishchaka, “Diagnostics of complex and rare abnormalities using ensemble decomposition learning”, In: International Conference on Computer and Computational Intelligence. Bangkok, Thailand; 2011, p. 19-26

[3] V.V. Gavrishchaka and O. Senyukova, “Robust algorithmic detection of the developed cardiac pathologies and emerging or transient abnormalities from short periods of RR data”, in AIP proceedings of 2011 International Symposium on Computational Models for Life Sciences (CMLS-11), Toyama City, Japan, 2011

[2] V.V. Gavrishchaka, M.E. Koepke, and O.N. Ulyanova, “Boosting-based discovery of multi-component physiological indicators: Applications to express diagnostics and personalized treatment optimization “, in ACM Proceedings of the 1-st International Health Informatics Symposium, Washington DC, 2010

[1] V.V. Gavrishchaka, M.E. Koepke, and O.N. Ulyanova, “Ensemble learning frameworks for the discovery of multi-component quantitative models in biomedical applications”, in IEEE Proceedings of the 2-nd International Conference on Computer Modeling and Simulation (ICCMS-2010)

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