---
license: apache-2.0
language: en
pretty_name: "Anode EHR Genomics v1: Deterministic Synthetic NSCLC Patient Records"
tags:
- medical
- healthcare
- genomics
- electronic-health-records
- ehr
- synthetic-data
- nsclc
- oncology
- clinical-trial-simulation
- tabular
- zero-seed
task_categories:
- tabular-classification
- tabular-regression
size_categories:
- 10K<n<100K
---
## 📖 Dataset Summary
This dataset contains high-fidelity, **deterministic synthetic patient records** for Non-Small Cell Lung Cancer (NSCLC). 

Unlike traditional generative AI that "guesses" data based on existing seeds, the **Anode Zero-Seed Engine** generates these records from first principles using medical logic, clinical guidelines, and genomic constraints. This ensures 100% biological and clinical consistency across all longitudinal fields.

## 🧬 Technical Specifications & Logic
Each record in this dataset represents a complex multimodal patient profile including histology, stage, genomic mutations, and corresponding treatment regimens.

- **Deterministic Integrity:** Genomic mutations (e.g., ALK fusion) are strictly mapped to their appropriate targeted therapies (e.g., Alectinib).
- **FHIR Alignment:** Data structures are engineered to be compatible with FHIR-standard medical interoperability protocols.
- **Noise Modeling:** The dataset includes realistic lab-result variance and sensor noise (Anode_Noise_v1) to simulate real-world data collection imperfections.

### Key Data Fields 
| Field | Description | Logic Mapping |
| :--- | :--- | :--- |
| `patient_id` | Longitudinal Identifier | Unique across Anode sequences |
| `driver_mutation` | Genomic marker (NGS) | EGFR, ALK, KRAS, ROS1, etc. |
| `pd_l1_tps_percent` | Immunotherapy biomarker | 0-100% scale |
| `first_line_regimen` | Clinical treatment | Targeted, Chemo, or Immunotherapy |
| `overall_survival` | Clinical outcome | Simulated based on histopathology |

---

## 🚀 Why Use Anode Synthetic Data?
1. **Zero Privacy Risk:** 100% synthetic. No real patient data was used as a seed, making it inherently compliant with HIPAA and GDPR.
2. **High-Density Edge Cases:** We explicitly engineer the data to include rare genomic-histology combinations that are underrepresented in public datasets.
3. **Sim-to-Real Optimized:** Our noise engine ensures that models trained on this data are robust enough for real-world clinical environments.

---

## 💼 Enterprise Customization & Contracts
The files hosted here serve as **Reference Batches** for architectural verification. 

Because medical and robotics data often require bespoke schemas, we operate on a **Contract-Based Delivery Model** rather than a generic SaaS subscription. We work directly with your engineering team to inject your specific logic requirements into our engine.

### How to Request a Custom Batch:
For production-grade volumes (100k-1M+ records) by our Zero-Seed framework with custom schema definitions:
1. **Define Requirements:** Identify the specific histology, genomic markers, or sensor types you need.
2. **Request Quote:** Email **[vesperbyar@gmail.com]** with your volume requirements.
3. **Delivery:** We deliver a tailored data contract with deterministic validation reports.

---

## ⚖️ Citation & Usage
**Simulator Source:** Cognisynth high-fidelity EHR/Genomics simulator v2026.2.
**Creator:** AnodeAI (Anode Synthesis).

*Disclaimer: This dataset is synthetic and intended for AI model training and software simulation only. It is not medical advice.*

**Copyright © 2026 AnodeAI. All Rights Reserved.**