| --- |
| 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 (1M Patients) |
|
|
| This dataset contains **1,000,000 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 1M 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 1,000,000 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_1m.csv") |
| print(df.shape) # (1000000, ~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. |
| |
| --- |
| |
| ## Citation |
| |
| Kartoun, U. (2025). Synthetic MELD-Plus (1M Patients). DBbun LLC. Inspired by: Kartoun et al., PLOS One (2017). |
