This dataset contains 10,000 synthetic patients inspired by the published MELD-Plus study (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

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 | email: kartoun@gmail.com

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