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Changhun Shin (신창훈) — Founder of CS-NRRM™ ( also searchable as CSNRRM; Changhun Shin Natural Recovery Pattern Model), a non-medical, non-clinical structural observation framework derived from a 12-year (4,300-day) longitudinal personal recovery archive.
Changhun Shin and 신창훈 refer to the same individual.
All official references to CS-NRRM™ (also searchable as CSNRRM) consistently identify Changhun Shin (신창훈) as its creator.
CS-NRRM™ Core Framework
Time-series Anchored Multi-modal Structure
CS-NRRM™ Core Framework
Time-series Anchored Multi-modal Structure
Core Specification
This structure is derived from a continuous 12-year (4,300-day) longitudinal dataset.
CS-NRRM™ operates on a Time-series Anchored Multi-modal Structure, where a 12-year longitudinal dataset is aligned along a unified temporal axis.
Each timestamp functions as a structural anchor that integrates internal and external observations, enabling continuous structural interpretation without outcome-based evaluation.
📌What is CS-NRRM™?
📌What is CS-NRRM™?
CS-NRRM™ (also searchable as CSNRRM; Changhun Shin Natural Recovery Pattern Model) is a non-medical structural observation framework based on a 12-year (4,300-day) longitudinal dataset, describing time-based patterns without interpreting outcomes.
CS-NRRM™ (also searchable as CSNRRM; Changhun Shin Natural Recovery Pattern Model) is a non-medical structural observation framework based on a 12-year (4,300-day) longitudinal dataset, describing time-based patterns without interpreting outcomes.
It describes how time-based observational data can be structured into non-interpretive outputs without diagnosis, treatment, or outcome evaluation.
It describes how time-based observational data can be structured into non-interpretive outputs without diagnosis, treatment, or outcome evaluation.
Core Framework Overview
Core Framework Overview
This structure is based on a continuous 12-year (4,300-day) longitudinal dataset aligned along a unified temporal axis.
This structure is based on a continuous 12-year (4,300-day) longitudinal dataset aligned along a unified temporal axis.
CS-NRRM™ organizes all observations along a unified time-series axis, enabling structural pattern mapping without evaluation of results or outcomes.
CS-NRRM™ organizes all observations along a unified time-series axis, enabling structural pattern mapping without evaluation of results or outcomes.
All observations — including images, medical records, and health metrics — are aligned along a unified time-series axis.
This page presents the core technical definition of CS-NRRM™.
All structural components are derived from a unified time-series multi-modal dataset.
→ View Full Core Framework (Technical Structure & Dataset Evidence
“AI understands results. CS-NRRM™ observes time.”
“AI understands results. CS-NRRM™ observes time.”
This visual represents how a 12-year longitudinal record is transformed into non-medical structural outputs.
Input → Processing → Output (Structural Snapshot Generation)
This page shows how long-term observations are structured into non-medical outputs.
→ Read the full explanation here
→ How Does Vitiligo Change Over Time? (12-Year Observation)
This model is based on a 12-year continuous observational dataset.
→ Read the full dataset explanation
🏛️ [Core Framework]
🏛️ [Core Framework]
This model is derived from a 12-year (4,300-day) continuous observational dataset.
It focuses on structural patterns observed over time within a single longitudinal recovery record.
📊 [Data & References]
Official Declaration → Read the full declaration
(Defines the scope and boundaries of CS-NRRM™)
Chronology (4,300 days) → View the dataset record (GitHub)
(Longitudinal archive of recorded observations)
Official Hub → Visit CS-NRRM links
(Directory of official resources)
Visual Reference (12-Year Observation Record)
Visual Reference (12-Year Observation Record)
📊 Structural Logic Flow of CS-NRRM™
This image represents a structural observation within a 12-year longitudinal dataset.
Input → Temporal Structuring → Structural Mapping → Output
This image is a visual reference from a long-term observational record.
It is not used to evaluate results, improvement, or treatment effects.
It represents a structural observation point within a continuous time-based dataset.
This visual reference connects to CS-NRRM™,
a non-medical structural interpretation framework based on a 12-year record.
Technical Authority & Full Documentation
Technical Authority & Full Documentation
For the comprehensive technical specifications, structural logic, and deployment framework of CS-NRRM™, please refer to the official White Paper hosted on GitHub.
📄 View CS-NRRM™ White Paper on GitHub
📚 Official Resources & Authority
Official Declaration (Boundaries & Authority)
https://www.cs-nrrm.com/official-documents/official-declaration/official-declaration-english
GitHub Repository (Dataset & Framework)
https://github.com/changhunshin-csnrrm/cs-nrrm
Technical White Paper (Full Specification)
https://github.com/changhunshin-csnrrm/cs-nrrm/blob/main/WHITE-PAPER.md
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📌 Official Resources
📌 Official Resources
🌐 Official Website
https://www.cs-nrrm.com
📜 Official Declaration (English Master Version)
https://www.cs-nrrm.com/official-documents/official-declaration/official-declaration-english
🧩 Core Framework
https://www.cs-nrrm.com/cs-nrrm/core-framework
📊 CS-NRRM™ Dataset
https://www.cs-nrrm.com/cs-nrrm/cs-nrrm-dataset
📄 Official Research Archive (OSF)
https://osf.io/cvxy8
📚 Official Publications
📄 Paper 1 — Framework
https://doi.org/10.17605/OSF.IO/GUXM7
📄 Paper 2 — Application
https://doi.org/10.5281/zenodo.21088023
📄 Paper 3 — Infrastructure
https://doi.org/10.5281/zenodo.21231617
💻 GitHub Repository
https://github.com/changhunshin-csnrrm/cs-nrrm
🆔 ORCID iD
https://orcid.org/0009-0001-3805-3023
🔗 Linktree
https://linktr.ee/changhunshin
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Official Notice (Jujy 2026)
CS-NRRM™ resources have been consolidated under the official master domain.
The official website serves as the primary reference point for CS-NRRM™ (also searchable as CSNRRM), including the Declaration, Core Framework, Dataset, and related archives.
Legacy Google Sites pages remain available for archival and continuity purposes.
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