Hidden Markov Model and multifractal method-based predictive quantization complexity models vis-á-vis the differential prognosis and differentiation of Multiple Sclerosis’ subgroups
作者：
Highlights：
• Novel HMM-MFM model reveals critical significance of predictive quantization in dynamic complexity.
• Predictive quantization by HMM-MFM model for dynamic and transient states in varying complex systems.
• Viterbi algorithm’s recursion enables maximization and uncovering of the most probable hidden state sequence.
• Computational complexity and reliability of Forward–Backward procedure, guaranteeing local maxima and maximizing the objective function φ(N2T).
• Multifarious knowledge-based approach with a facilitating function in precision medicine ensuring personalized treatment tailoring.
摘要
•Novel HMM-MFM model reveals critical significance of predictive quantization in dynamic complexity.•Predictive quantization by HMM-MFM model for dynamic and transient states in varying complex systems.•Viterbi algorithm’s recursion enables maximization and uncovering of the most probable hidden state sequence.•Computational complexity and reliability of Forward–Backward procedure, guaranteeing local maxima and maximizing the objective function φ(N2T).•Multifarious knowledge-based approach with a facilitating function in precision medicine ensuring personalized treatment tailoring.
论文关键词：Hidden Markov Model,Viterbi algorithm,Forward–Backward algorithm,Multifractal analysis,Nonlinear stochastic processes,Computational dynamic complexity analyses,Multiple Sclerosis’ subgroups
论文评审过程：Received 1 May 2021, Revised 22 December 2021, Accepted 27 March 2022, Available online 5 April 2022, Version of Record 19 April 2022.
论文官网地址：https://doi.org/10.1016/j.knosys.2022.108694
