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| Format: | Recurso digital |
| Language: | German |
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2026
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| Online Access: | https://doi.org/10.5281/zenodo.18383310 |
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| _version_ | 1866902030336917504 |
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| author | Fuerste, Dietmar |
| author_facet | Fuerste, Dietmar |
| contents | <p>This repository contains the technical documentation (Version 9.46) of the AI.LC-Analyzer, a deterministic, mechanistic system designed to map and analyze the causal dynamics of chronic diseases such as Long COVID and ME/CFS.</p> <p> </p> <p>The AI.LC-Analyzer generates high-resolution, time-explicit system trajectories within a fixed, physiologically constrained landscape. Its primary purpose is to support scientific understanding of system behavior, stability regimes, failure events, and recovery boundaries in complex chronic disease processes. The system is not designed for prediction, optimization, or automated clinical decision-making.</p> <p> </p> <p>A central architectural principle is the strict separation between core system dynamics and epistemic observation. After landscape calibration (Phase 1), the generative core is frozen and remains non-adaptive. Machine learning interfaces introduced in Phase 2 operate exclusively in a read-only mode, enabling pattern recognition and trajectory analysis without any feedback into the system. Conceptual provisions for Phase 3 restrict reinforcement learning to offline navigation and sequencing tasks, explicitly excluding optimization of system parameters, states, or attractor configurations.</p> <p> </p> <p>Diagnostic outcomes and failure states are treated as explanatory descriptors rather than labels or rewards. Evidence is defined as an intermediate, machine-readable layer linking system dynamics to interpretation while preserving causal traceability.</p> <p> </p> <p>This document provides the stabilized technical specification of the AI.LC-Analyzer, including system architecture, trajectory semantics, evidence logic, and learning governance constraints. It is intended as a scientific reference for systems medicine, computational modeling, and machine learning research.</p> <p> </p> <p>Disclosure:</p> <p>This document is published as a public prior-art disclosure. It describes a non-optimizing, mechanistic system architecture for the analysis of chronic disease dynamics and does not constitute a clinical decision or diagnostic system.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18383310 |
| institution | Zenodo |
| language | deu |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI.LC-Analyzer – Technical Documentation V9.46. - Phase 1: A mechanistic system architecture for trajectory-based analysis of chronic disease dynamics Fuerste, Dietmar Long COVID Artificial intelligence Artificial Intelligence Artifical Intelligence Machine learning Machine Learning Long covid ME/CFS Fatigue/immunology Fatigue/nursing Fatigue/virology Fatigue PEM <p>This repository contains the technical documentation (Version 9.46) of the AI.LC-Analyzer, a deterministic, mechanistic system designed to map and analyze the causal dynamics of chronic diseases such as Long COVID and ME/CFS.</p> <p> </p> <p>The AI.LC-Analyzer generates high-resolution, time-explicit system trajectories within a fixed, physiologically constrained landscape. Its primary purpose is to support scientific understanding of system behavior, stability regimes, failure events, and recovery boundaries in complex chronic disease processes. The system is not designed for prediction, optimization, or automated clinical decision-making.</p> <p> </p> <p>A central architectural principle is the strict separation between core system dynamics and epistemic observation. After landscape calibration (Phase 1), the generative core is frozen and remains non-adaptive. Machine learning interfaces introduced in Phase 2 operate exclusively in a read-only mode, enabling pattern recognition and trajectory analysis without any feedback into the system. Conceptual provisions for Phase 3 restrict reinforcement learning to offline navigation and sequencing tasks, explicitly excluding optimization of system parameters, states, or attractor configurations.</p> <p> </p> <p>Diagnostic outcomes and failure states are treated as explanatory descriptors rather than labels or rewards. Evidence is defined as an intermediate, machine-readable layer linking system dynamics to interpretation while preserving causal traceability.</p> <p> </p> <p>This document provides the stabilized technical specification of the AI.LC-Analyzer, including system architecture, trajectory semantics, evidence logic, and learning governance constraints. It is intended as a scientific reference for systems medicine, computational modeling, and machine learning research.</p> <p> </p> <p>Disclosure:</p> <p>This document is published as a public prior-art disclosure. It describes a non-optimizing, mechanistic system architecture for the analysis of chronic disease dynamics and does not constitute a clinical decision or diagnostic system.</p> |
| title | AI.LC-Analyzer – Technical Documentation V9.46. - Phase 1: A mechanistic system architecture for trajectory-based analysis of chronic disease dynamics |
| topic | Long COVID Artificial intelligence Artificial Intelligence Artifical Intelligence Machine learning Machine Learning Long covid ME/CFS Fatigue/immunology Fatigue/nursing Fatigue/virology Fatigue PEM |
| url | https://doi.org/10.5281/zenodo.18383310 |