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Designing efficient interventions for pre-disease states using control theory
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DOI：10.1587/nolta.17.156.png)
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Abstract
En 中文
To extend healthy life expectancy in an aging society, it is crucial to prevent various diseases at pre-disease states. Although dynamical network biomarker theory has been developed for pre-disease detection, mathematical frameworks for pre-disease treatment have not been well established. Here I propose a control theory-based approach for pre-disease treatment, named Markov chain sparse control (MCSC), where time evolution of a probability distribution on a Markov chain is described as a discrete-time linear system. By designing a sparse controller, a few candidate states for intervention are identified. The validity of MCSC is demonstrated using numerical simulations and real-data analysis.
Keywords:
pre-disease state
control theory
Markov chain
intervention
sparse matrix
Markov chain sparse control
Journal
I
IF:
0.6
Papers:
39
Citations:
246