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README.md

@@ -14,6 +14,7 @@ tags:
   - ehr
   - electronic-health-records
   - synthea
+  - deepneuro
 size_categories:
   - 100K<n<1M
 ---
@@ -22,6 +23,8 @@ size_categories:
 
 A comprehensive synthetic healthcare dataset containing **575,415 patients** with complete medical histories, generated using [Synthea](https://github.com/synthetichealth/synthea) - an open-source synthetic patient generator.
 
+**Dataset Curator:** [Richard Young](https://deepneuro.ai/richard) | [DeepNeuro.AI](https://deepneuro.ai)
+
 ## Dataset Description
 
 This dataset provides realistic but entirely synthetic patient records suitable for:
@@ -58,6 +61,37 @@ This dataset provides realistic but entirely synthetic patient records suitable
 
 **Total Size:** 134GB (Parquet), 977GB (CSV source)
 
+## Data Processing Pipeline
+
+### Generation Process
+The dataset was generated using a batched approach to handle the scale:
+- **20 batches** of 25,000 patients each
+- **40GB Java heap** allocation per batch
+- **32 CPU cores** for parallel generation
+- Custom batch merging to preserve CSV headers (avoiding Synthea's append_mode bug)
+
+### Compression & Optimization
+The raw CSV output (977GB) was converted to Parquet format achieving **86% compression**:
+
+| Format | Size | Compression |
+|--------|------|-------------|
+| CSV (raw) | 977 GB | - |
+| Parquet | 134 GB | 86% reduction |
+
+**Conversion method:** Memory-efficient streaming using Polars `scan_csv()` + `sink_parquet()`:
+- Processes files in chunks without loading entire files into memory
+- Handles 30GB+ CSV files without OOM errors
+- Schema overrides for mixed-type columns (claims, procedures, observations)
+- 8 parallel workers for optimal throughput
+
+### Data Verification
+Data integrity was verified through:
+1. **Header Validation:** All 19 CSV files confirmed to have correct headers
+2. **Row Count Verification:** Patient counts validated at each batch merge
+3. **Parquet Integrity:** All 18 Parquet files successfully written and readable
+4. **Foreign Key Validation:** Patient IDs verified across related tables
+5. **Schema Consistency:** Column types verified during Parquet conversion
+
 ## Data Schema
 
 ### patients.parquet
@@ -160,11 +194,11 @@ This dataset provides realistic but entirely synthetic patient records suitable
 import polars as pl
 
 # Load a single table
-patients = pl.read_parquet("hf://datasets/YOUR_USERNAME/synthea-575k/patients.parquet")
+patients = pl.read_parquet("hf://datasets/richardyoung/synthea-575k-patients/data/patients.parquet")
 
 # Load and join tables
-encounters = pl.read_parquet("hf://datasets/YOUR_USERNAME/synthea-575k/encounters.parquet")
-conditions = pl.read_parquet("hf://datasets/YOUR_USERNAME/synthea-575k/conditions.parquet")
+encounters = pl.read_parquet("hf://datasets/richardyoung/synthea-575k-patients/data/encounters.parquet")
+conditions = pl.read_parquet("hf://datasets/richardyoung/synthea-575k-patients/data/conditions.parquet")
 
 # Example: Get all conditions for a patient
 patient_conditions = conditions.filter(pl.col("PATIENT") == "some-uuid")
@@ -174,7 +208,14 @@ patient_conditions = conditions.filter(pl.col("PATIENT") == "some-uuid")
 # Using pandas
 import pandas as pd
 
-patients = pd.read_parquet("hf://datasets/YOUR_USERNAME/synthea-575k/patients.parquet")
+patients = pd.read_parquet("hf://datasets/richardyoung/synthea-575k-patients/data/patients.parquet")
+```
+
+```python
+# Using datasets library
+from datasets import load_dataset
+
+dataset = load_dataset("richardyoung/synthea-575k-patients", data_files="data/patients.parquet")
 ```
 
 ## Demographics Summary
@@ -194,6 +235,7 @@ Based on 575,415 synthetic patients:
 - **Configuration:** Full clinical documentation enabled
 - **Modules:** 89 disease modules, 157 submodules
 - **Seed Range:** 3000-22000 (20 batches of 25K patients)
+- **Hardware:** 62GB RAM, 32 CPU cores, 40GB Java heap
 
 ## Limitations
 
@@ -204,14 +246,16 @@ Based on 575,415 synthetic patients:
 
 ## Citation
 
-If you use this dataset, please cite:
+If you use this dataset, please cite both the dataset and Synthea:
 
 ```bibtex
-@misc{synthea575k,
+@misc{young2025synthea575k,
   title={Synthea Synthetic Patient Records (575K Patients)},
-  author={Generated with Synthea},
+  author={Young, Richard},
   year={2025},
-  howpublished={Hugging Face Datasets},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/datasets/richardyoung/synthea-575k-patients}},
+  note={Generated using Synthea. Curated by DeepNeuro.AI}
 }
 
 @article{walonoski2018synthea,
@@ -221,7 +265,9 @@ If you use this dataset, please cite:
   volume={25},
   number={3},
   pages={230--238},
-  year={2018}
+  year={2018},
+  publisher={Oxford University Press},
+  doi={10.1093/jamia/ocx079}
 }
 ```
 
@@ -231,5 +277,12 @@ This dataset is released under the MIT License, consistent with Synthea's licens
 
 ## Acknowledgments
 
-- [Synthea](https://github.com/synthetichealth/synthea) - The MITRE Corporation
-- Generated using Synthea's open-source synthetic patient generator
+- **Dataset Curation:** [Richard Young](https://deepneuro.ai/richard) - [DeepNeuro.AI](https://deepneuro.ai)
+- **Data Generation:** [Synthea](https://github.com/synthetichealth/synthea) - The MITRE Corporation
+- **Processing Tools:** [Polars](https://pola.rs/) for memory-efficient data processing
+
+## Contact
+
+For questions or issues with this dataset, please contact:
+- Richard Young: [deepneuro.ai/richard](https://deepneuro.ai/richard)
+- Open an issue on this dataset's community tab