Prognostic medication: for predicting premonition and recovery

Remi Konagaya, Ken Naitoh, Keisuke Suzuki, Hiroshi Takashima

    Research output: Contribution to journalArticle

    Abstract

    A nonlinear ordinary differential equation model of time-dependent features of six macroscopic molecular groups on information and function interacting in living beings (Naitoh in Jpn J Ind Appl Math 28:15–26, 2011; Naitoh and Inoue in J Artif Life Robot 18:127–132, 2013) brings a possibility for predicting the morphogenetic process and sustainment of human beings. In this report, along with the number theory, we derive mathematical conditions for predicting the premonition just before sickness, which are logically derived from the differential equation model, and also agree with computational results of death and apparent death obtained by numerically solving the equation model. The mathematical conditions derived agree with an important knowledge revealed by Chen (Proceedings of the international symposium on artificial life and robotics (AROB20th), Beppu, Oita Japan, pp 127–132, 2015), i.e., a critical condition on densities of healthy molecules just before sickness. This agreement is an evidence for significance of our nonlinear equation model and the linear analysis done by Chen et al. Furthermore, we also derive a two-step mathematical condition for classifying death and apparent death. These conditions lead to a method for acquiring premonition of illness and a method for recovering from illness. Finally, we show a possibility that this equation model, extended with random noise from environmental disturbance, may predict life pattern concerning periods of sickness, while also considering polymorphism.

    Original languageEnglish
    Pages (from-to)1-8
    Number of pages8
    JournalArtificial Life and Robotics
    DOIs
    Publication statusAccepted/In press - 2017 Jul 4

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    Keywords

    • Mathematical condition
    • Premonition of illness
    • Prognostic medication
    • Random noise
    • Recovery

    ASJC Scopus subject areas

    • Biochemistry, Genetics and Molecular Biology(all)
    • Artificial Intelligence

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