---
license: other
license_name: commercial
license_link: LICENSE
task_categories:
- text-generation
- feature-extraction
- summarization
- tabular-to-text
- table-to-text
- text-retrieval
tags:
- medical
- meld
- nlp
- manuscript
- emrs
- ehrs
- rwd
- rwe
- harvard
- ibm
- mgb
- mgh
- liver
- hepatology
- predict
- unos
---

# Synthetic MELD-Plus (10K Patients)

[Watch a demo](https://www.youtube.com/watch?v=VRGncTQRmiE)

This dataset contains **10,000 synthetic patients** inspired by the published [MELD-Plus study](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0186301) (a collboration between Massachusetts General Hospital and IBM Research). Each row corresponds to a single admission, with demographics, labs, comorbidities, medications, derived scores (MELD, MELD-Na, MELD-Plus), and the binary outcome **Death_Within_90_Days**.  

All data are **artificially generated** and contain **no identifiable patient records**.

---

## Source and Augmentation

- **Original study:** The MELD-Plus study described ~5,000 admissions across its main manuscript and four supplementary documents. These reported **summary statistics only** (means, SDs, prevalences, ranges, quartiles, and units).  
- **Augmentation process to 10K patients:**  
  1. **Extracted variables** (covariates, outcomes, descriptive stats) from main + supplementary files.  
  2. **Simulated distributions** for continuous labs (Normal with reported mean/SD, with physiologic plausibility bounds).  
  3. **Applied prevalence rates** for comorbidities (zero-inflated Poisson) and for missingness in labs.  
  4. **Modeled medications** with Poisson counts.  
  5. **Computed derived scores:** MELD, MELD-Na, MELD-Plus.  
  6. **Generated outcomes:** Death_Within_90_Days simulated via MELD-Plus logistic model, calibrated to match ~16.3% mortality.  
  7. **Scaled up** to 10,000 patients, each with one admission, preserving distributions and correlations.  

---

## Schema (Highlights)

- **Demographics:** Age, Gender, Ethnicity, MaritalStatus, BMI, Insurance (Medicaid/Medicare/Other), Admissions_Prior12mo  
- **Labs:** TotalBilirubin, Creatinine, INR, Sodium, Albumin, WBC  
- **Comorbidities:** 20+ variables (e.g., Ascites, HepaticEncephalopathy, Diabetes, Hypertension, COPD)  
- **Medications:** Anticoagulants, Antiplatelets, Antiarrhythmics_Diuretics, Aspirin, Cardiovascular, DiabetesMeds, etc.  
- **Derived:** MELD, MELD_Na, MELD_Plus, OnDialysis, Death_Within_90_Days  

---

## Example Usage

```python
import pandas as pd

df = pd.read_csv("meldplus_synthetic_10k.csv")
print(df.shape)   # (10000, ~50 columns)
print(df.head())
```

---

## Intended Use

- **Educational & personal learning**  
- **Benchmarking methods** for EMR preprocessing, feature extraction, and survival analysis  
- **Synthetic data methodology testing**  

Not for clinical decision-making.  

---

## License

This dataset is released under a **Proprietary License**:  

- **Free for personal use**:  
  You may download, explore, and use this dataset for personal, non-commercial purposes (e.g., self-learning, academic curiosity, experimentation).  

- **Commercial use requires a paid license**:  
  Any use in commercial settings requires explicit purchase of a license from the author. Examples include, but are not limited to:  
  - Teaching or use in paid courses or workshops  
  - Use in client-facing presentations or consulting projects  
  - Commercial analyses or data services  
  - Integration into software products, apps, or platforms  

- **Redistribution prohibited**:  
  You may not redistribute the dataset or host it elsewhere, whether for free or for sale.  

To obtain a **commercial license**, please contact:  
**Uri Kartoun** — [LinkedIn](https://www.linkedin.com/in/kartoun/) | email: *kartoun@gmail.com*