YPL67/DPR

Diabetic Patient 30-Day Readmission Dataset

Dataset Summary

This dataset is derived from the Kaggle Diabetic Patients Readmission Prediction dataset. The original dataset contains electronic health record data from diabetic patient hospital encounters and is commonly used for hospital readmission prediction.

This released version organizes the repository into three levels of data:

1. 1.Raw Data: the original downloaded source files.
2. 2.Intermediate Data: derived files before final model-ready preprocessing, including cohort, feature table, and train/validation/test splits before imputation and one-hot encoding.
3. 3.Preprocessed Data: final model-ready train/validation/test files after imputation, one-hot encoding, column alignment, and label attachment.

The dataset is formatted for binary classification of 30-day hospital readmission and provides reproducible train, validation, and test splits for evaluating clinical prediction models and uncertainty quantification methods.

Source Data

Original source:

• —Kaggle: https://www.kaggle.com/datasets/saurabhtayal/diabetic-patients-readmission-prediction

Prediction Task

The prediction task is binary classification.

Target variable:

• —readmit_30d

Label definition:

• —1: patient was readmitted within 30 days, corresponding to readmitted == "<30"
• —0: patient was not readmitted within 30 days, corresponding to readmitted == ">30" or readmitted == "NO"

The original multiclass readmitted variable was converted into this binary target.

Repository Structure

.
├── README.md
├── Raw Data/
│   ├── diabetic_data.csv
│   ├── IDS_mapping.csv
│   ├── Data Dictionary.png
│   └── other original source files, if present
├── Intermediate Data/
│   ├── cohort.csv
│   ├── features.csv
│   ├── train_raw.csv
│   ├── validation_raw.csv
│   └── test_raw.csv
└── Preprocessed Data/
    ├── train.csv
    ├── validation.csv
    └── test.csv