Development and Interpretation of a Deep Learning Algorithm for Multi-State Model Analysis

Majeed, Najwan

doi:10.25673/122116

Proceedings of International Conference on Applied Innovation in IT · 2025/08/29 · Vol. 13 · Issue 4 · pp. 213–221

Development and Interpretation of a Deep Learning Algorithm for Multi-State Model Analysis

Najwan Wafeek Majeed

📄 Download PDF DOI: 10.25673/122116

Abstract

This research presents the development of a deep learning algorithm grounded in hidden variable models for the analysis of multi-state systems. Accurately estimating the joint probability distribution of interrelated variables remains a significant challenge, particularly in scenarios where the state data is either unavailable or only weakly linked to the observed correlation data. Hidden variable models address this problem by introducing latent or unobserved factors that can effectively capture and explain the variations in the observed data. System identification, which focuses on developing mathematical models of dynamic systems based on observed input-output data, shows a natural synergy with machine learning methodologies. By leveraging this connection, we propose structural simplifications to hidden variable models that aim to improve their capacity to represent critical state variables and enhance their performance in system identification applications. These simplifications make the models more interpretable and computationally tractable, while still preserving the essential dynamics of multi-state systems, thereby improving the reliability and practicality of their deployment.

Keywords

Deep Learning Structured Hidden Variable Models Multi-State Models Nonlinear System Identification Generative Models.

References

K. Hua, D. Wojdyla, A. Carnicelli, C. Granger, X. Wang, and H. Hong, "Network meta-analysis with individual participant-level data of time-to-event outcomes using Cox regression," Statistical Methods in Medical Research, vol. 44, no. 5, p. e70027, Feb. 2025, doi: 10.1002/sim.70027.recurrence score and clinical-pathological features to individualize prognosis and prediction of chemotherapy benefit in early breast cancer,” J. Clin. Oncol., vol. 39, no. 6, pp. 557–564, 2021.
J. A. Sparano, M. R. Crager, G. Tang, R. J. Gray, S. M. Stemmer, and S. Shak, “Development and validation of a tool integrating the 21-gene recurrence score and clinical-pathological features to individualize prognosis and prediction of chemotherapy benefit in early breast cancer,” J. Clin. Oncol., vol. 39, no. 6, pp. 557–564, 2021.
M. J. Ramezankhani, M. Blaha, M. Mirbolouk, F. Azizi, and F. Hadaegh, “Multi-state analysis of hypertension and mortality: Application of semi-Markov model in a longitudinal cohort study,” BMC Cardiovasc. Disord., vol. 20, no. 1, pp. 1–13, 2020.
J. D. Kalbfleisch and R. L. Prentice, The Statistical Analysis of Failure Time Data, 2nd ed. Hoboken, NJ, USA: John Wiley & Sons, 2020.
P. K. Andersen, Ø. Borgan, R. D. Gill, and N. Keiding, Statistical Models Based on Counting Processes. Berlin, Germany: Springer Science & Business Media, 2023.
L. Meira-Machado and M. Sestelo, “Estimation in the progressive illness-death model: A nonexhaustive review,” Biometrical J., vol. 61, no. 2, pp. 245–263, 2019.
Z. Yan, J. Liu, L. T. Yang, and N. Chawla, “Big data fusion in Internet of Things,” Information Fusion, vol. 100, pp. 32–33, 2022.
H. Zenati, C. S. Foo, B. Lecouat, G. Manek, and V. R. Chandrasekhar, “Efficient GAN-based anomaly detection,” Inst. for Infocomm Res., Singapore, School of Computer Science, Nanyang Technol. Univ., 2018.
M. Ahmed, A. N. Mahmood, and J. Hu, “A survey of network anomaly detection techniques,” J. Netw. Comput. Appl., vol. 60, pp. 19–31, 2020.
R. Daneshfaraz, E. Aminvash, A. Ghaderi, J. Abraham, and M. Bagherzadeh, “SVM performance for predicting the effect of horizontal screen diameters on the hydraulic parameters of a vertical drop,” Appl. Sci., vol. 11, p. 4238, 2021.