Predictive Medicine Requires System Dynamics Three Emerging Paths Toward Dynamic Disease Modeling
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2026
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| _version_ | 1866901211690565632 |
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| author | Domargård, Anita |
| author_facet | Domargård, Anita |
| contents | <p>Predictive medicine has become a central goal of modern healthcare, yet most predictive approaches still rely on static models of disease. Biomarkers, risk scores, and machine-learning predictions are typically derived from cross-sectional data or simplified linear assumptions about disease progression. Such approaches often fail to capture the temporal and dynamic nature of biological systems.</p> <p>This article argues that predictive medicine requires an explicit integration of <strong>system dynamics</strong>. Chronic diseases do not evolve as static states but as <strong>time-dependent processes</strong> characterized by instability, transitions, and shifting regulatory regimes. Early-warning signals such as rising variance, increasing autocorrelation, delayed recovery, and network synchronization have been described in complex systems science and may provide a conceptual framework for understanding disease trajectories.</p> <p>Three emerging paths toward dynamic disease modeling are examined:</p> <ol> <li> <p><strong>Systems biology approaches</strong>, which attempt to map interacting molecular networks but often remain limited to static representations of complex biological processes.</p> </li> <li> <p><strong>AI-driven predictive models</strong>, which can detect statistical patterns in large datasets but frequently lack mechanistic interpretability and dynamic physiological context.</p> </li> <li> <p><strong>Complex systems and dynamical frameworks</strong>, which interpret disease as a process unfolding near stability boundaries, where transitions, tipping points, and regime shifts may occur.</p> </li> </ol> <p>The article proposes that these approaches converge toward a new paradigm in which disease is interpreted not as a fixed condition but as a <strong>dynamic process evolving through phases of stability, instability, and transition</strong>. Within this perspective, predictive medicine becomes the task of identifying <strong>when systems approach critical thresholds</strong>, rather than merely classifying static disease states.</p> <p>The Universal Resonance Model (URM) provides one conceptual framework for such an interpretation by viewing disease progression as a form of <strong>system resonance and attractor reconfiguration across biological scales</strong>. In this view, predictive signals emerge from measurable changes in system dynamics rather than isolated biomarkers.</p> <p>Understanding disease through dynamic systems theory may help explain phenomena that remain difficult to interpret in conventional models, including delayed treatment responses, heterogeneous therapeutic outcomes, and apparent placebo responses in clinical trials.</p> <p>Developing predictive medicine therefore requires a shift from static measurement toward <strong>temporal, dynamic, and systems-level analysis of disease processes</strong>.</p> <p>This article is part of the Universal Resonance Model (URM) research series on dynamic disease systems.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19032485 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Predictive Medicine Requires System Dynamics Three Emerging Paths Toward Dynamic Disease Modeling Domargård, Anita Predictive medicine System dynamics Dynamic disease modeling Complex systems medicine Disease transitions Early-warning signals Critical transitions Systems biology Artificial intelligence in medicine Universal Resonance Model Medicine Systems Biology Complex Systems Artificial Intelligence Computational Biology Clinical Medicine <p>Predictive medicine has become a central goal of modern healthcare, yet most predictive approaches still rely on static models of disease. Biomarkers, risk scores, and machine-learning predictions are typically derived from cross-sectional data or simplified linear assumptions about disease progression. Such approaches often fail to capture the temporal and dynamic nature of biological systems.</p> <p>This article argues that predictive medicine requires an explicit integration of <strong>system dynamics</strong>. Chronic diseases do not evolve as static states but as <strong>time-dependent processes</strong> characterized by instability, transitions, and shifting regulatory regimes. Early-warning signals such as rising variance, increasing autocorrelation, delayed recovery, and network synchronization have been described in complex systems science and may provide a conceptual framework for understanding disease trajectories.</p> <p>Three emerging paths toward dynamic disease modeling are examined:</p> <ol> <li> <p><strong>Systems biology approaches</strong>, which attempt to map interacting molecular networks but often remain limited to static representations of complex biological processes.</p> </li> <li> <p><strong>AI-driven predictive models</strong>, which can detect statistical patterns in large datasets but frequently lack mechanistic interpretability and dynamic physiological context.</p> </li> <li> <p><strong>Complex systems and dynamical frameworks</strong>, which interpret disease as a process unfolding near stability boundaries, where transitions, tipping points, and regime shifts may occur.</p> </li> </ol> <p>The article proposes that these approaches converge toward a new paradigm in which disease is interpreted not as a fixed condition but as a <strong>dynamic process evolving through phases of stability, instability, and transition</strong>. Within this perspective, predictive medicine becomes the task of identifying <strong>when systems approach critical thresholds</strong>, rather than merely classifying static disease states.</p> <p>The Universal Resonance Model (URM) provides one conceptual framework for such an interpretation by viewing disease progression as a form of <strong>system resonance and attractor reconfiguration across biological scales</strong>. In this view, predictive signals emerge from measurable changes in system dynamics rather than isolated biomarkers.</p> <p>Understanding disease through dynamic systems theory may help explain phenomena that remain difficult to interpret in conventional models, including delayed treatment responses, heterogeneous therapeutic outcomes, and apparent placebo responses in clinical trials.</p> <p>Developing predictive medicine therefore requires a shift from static measurement toward <strong>temporal, dynamic, and systems-level analysis of disease processes</strong>.</p> <p>This article is part of the Universal Resonance Model (URM) research series on dynamic disease systems.</p> |
| title | Predictive Medicine Requires System Dynamics Three Emerging Paths Toward Dynamic Disease Modeling |
| topic | Predictive medicine System dynamics Dynamic disease modeling Complex systems medicine Disease transitions Early-warning signals Critical transitions Systems biology Artificial intelligence in medicine Universal Resonance Model Medicine Systems Biology Complex Systems Artificial Intelligence Computational Biology Clinical Medicine |
| url | https://doi.org/10.5281/zenodo.19032485 |