Abstract
This study presents a new hybrid approach to predictive modelling of disease dynamics for finding optimal therapy. We use existing methods, such as expert-based modelling methods, models of system dynamics and ML methods in compositions together with our proposed modelling methods for simulating treatment process and predicting treatment outcomes depending on the different therapy types. Treatment outcomes include a set of treatment-goal values, therapy types include a combination of drugs and treatment procedures. Personal therapy recommendation by this approach is optimal in terms of achieving the best treatment multipurpose outcomes. We use this approach in the task of creating a practical tool for finding optimal therapy for T2DM disease. The proposed tool was validated using surveys of experts, clinical recommendations [1], and classic metrics for predictive task. All these validations have shown that the proposed tool is high-quality, interpretable and usability, therefore it can be used as part of the Decision Support System for medical specialists who work with T2DM patients.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dedov, I.I., et al.: Standards of specialized diabetes care. In: Dedov, I.I., Shestakova M.V., Mayorov A.Yu Diabetes Mellit, 9th ed. (2019)
Kumar, A.: Optimizing antimicrobial therapy in sepsis and septic shock. Crit. Care Clin. 25, 733–751 (2009)
Srividya, P., Devi, T.S.R., Gunasekaran, S.: Ftir spectral study on diabetic blood samples – monotherapy and combination therapy. Ojp 25, 744–750 (2012)
Sicklick, J.K., et al.: Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study. Nat. Med. 30, 289–299 (2019)
Burgmaier, M., Heinrich, C., Marx, N.: Cardiovascular effects of GLP-1 and GLP-1-based therapies: implications for the cardiovascular continuum in diabetes? Diab. Med. 30, 289–299 (2013)
Menden, M.P., et al.: Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties. PLoS One 8, e61318 (2013)
Khaledi, A., et al.: Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics. EMBO Mol. Med. 12, e10264 (2020)
Barbieri, C., et al.: A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis. Comput. Biol. Med. 61, 56–61 (2015)
Janizek, J.D., Celik, S., Lee, S.I.: Explainable machine learning prediction of synergistic drug combinations for precision cancer medicine. bioRxiv 8, 1-115 (2018)
Preuer, K., Lewis, R., Hochreiter, S., Bender, A., Bulusu, K., Klambauer, G.: DeepSynergy: predicting anti-cancer drug synergy with Deep Learning. Bioinformatics 34(9), 1538–1546 (2018)
Tabl, A., Alkhateeb, A., Pham, H., Rueda, L., ElMaraghy, W., Ngom, A.: A novel approach for identifying relevant genes for breast cancer survivability on specific therapies. Evol. Bioinf. 14, 117693431879026 (2018)
Kuenzi, B., et al.: Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell 38(5), 672-684.e6 (2020). https://doi.org/10.1016/j.ccell.2020.09.014
Butler, J., Januzzi, J.L., Rosenstock, J.: Management of heart failure and type 2 diabetes mellitus: Maximizing complementary drug therapy. Diab. Obes. Metab. 22, 1243–1262 (2020)
Bajusz, D., Rácz, A., Héberger, K.: Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J. Cheminf. 7(1), 1–13 (2015)
Acknowledgments
The reported study was funded by RFBR according to the research project № 20–31-70001. Participation in the ICCS conference was supported by the NWO Science Diplomacy Fund project # 483.20.038 ‘‘Russian-Dutch Collaboration in Computational Science’’.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Pavlovskii, V.V., Derevitskii, I.V., Kovalchuk, S.V. (2021). Hybrid Predictive Modelling for Finding Optimal Multipurpose Multicomponent Therapy. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12744. Springer, Cham. https://doi.org/10.1007/978-3-030-77967-2_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-77967-2_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77966-5
Online ISBN: 978-3-030-77967-2
eBook Packages: Computer ScienceComputer Science (R0)