aai540-group3/diabetes-readmission

aai540-group3/diabetes-readmission

Model Description

This model predicts 30-day hospital readmissions for diabetic patients using historical patient data and machine learning techniques. The model aims to identify high-risk individuals enabling targeted interventions and improved healthcare resource allocation.

Overview

• —Task: Binary Classification (Hospital Readmission Prediction)
• —Model Type: autogluon
• —Framework: Python Autogluon
• —License: MIT
• —Last Updated: 2024-10-29

Performance Metrics

• —Test Accuracy: 0.8865
• —Test ROC-AUC: 0.6467

Feature Importance

Significant features and their importance scores:

Note: Only features with non-zero importance are shown. The confidence intervals (CI) are calculated at the 99% level. Features with p-value < 0.05 are considered statistically significant.

Features

Numeric Features

• —Patient demographics (age)
• —Hospital stay metrics (timeinhospital, numprocedures, numlab_procedures)
• —Medication metrics (nummedications, totalmedications)
• —Service utilization (numberoutpatient, numberemergency, number_inpatient)
• —Diagnostic information (number_diagnoses)

Binary Features

• —Patient characteristics (gender)
• —Medication flags (diabetesmed, change, insulinwithoral)

Interaction Features

• —Time-based interactions (medications × time, procedures × time)
• —Complexity indicators (age × diagnoses, medications × procedures)
• —Resource utilization (lab procedures × time, medications × changes)

Ratio Features

• —Resource efficiency (procedure/medication ratio, lab/procedure ratio)
• —Diagnostic density (diagnosis/procedure ratio)

Intended Use

This model is designed for healthcare professionals to assess the risk of 30-day readmission for diabetic patients. It should be used as a supportive tool in conjunction with clinical judgment.

Primary Intended Uses

• —Predict likelihood of 30-day hospital readmission
• —Support resource allocation and intervention planning
• —Aid in identifying high-risk patients
• —Assist in care management decision-making

Out-of-Scope Uses

• —Non-diabetic patient populations
• —Predicting readmissions beyond 30 days
• —Making final decisions without clinical oversight
• —Use as sole determinant for patient care decisions
• —Emergency or critical care decision-making

Training Data

The model was trained on the Diabetes 130-US Hospitals Dataset (1999-2008) from UCI ML Repository. This dataset includes:

• —Over 100,000 hospital admissions
• —50+ features including patient demographics, diagnoses, procedures
• —Binary outcome: readmission within 30 days
• —Comprehensive medication tracking
• —Detailed hospital utilization metrics

Training Procedure

Data Preprocessing

• —Missing value imputation using mean/mode
• —Outlier handling using 5-sigma clipping
• —Feature scaling using StandardScaler
• —Categorical encoding using one-hot encoding
• —Log transformation for skewed features

Feature Engineering

• —Created interaction terms between key variables
• —Generated resource utilization ratios
• —Aggregated medication usage metrics
• —Developed time-based interaction features
• —Constructed diagnostic density metrics

Model Training

• —Data split: 70% training, 15% validation, 15% test
• —Cross-validation for model selection
• —Hyperparameter optimization via grid search
• —Early stopping to prevent overfitting
• —Model selection based on ROC-AUC performance

Limitations & Biases

Known Limitations

• —Model performance depends on data quality and completeness
• —Limited to the scope of training data timeframe (1999-2008)
• —May not generalize to significantly different healthcare systems
• —Requires standardized input data format

Potential Biases

• —May exhibit demographic biases present in training data
• —Performance may vary across different hospital systems
• —Could be influenced by regional healthcare practices
• —Might show temporal biases due to historical data

Recommendations

• —Regular model monitoring and retraining
• —Careful validation in new deployment contexts
• —Assessment of performance across demographic groups
• —Integration with existing clinical workflows

Monitoring & Maintenance

Monitoring Requirements

• —Track prediction accuracy across different patient groups
• —Monitor input data distribution shifts
• —Assess feature importance stability
• —Evaluate performance metrics over time

Maintenance Schedule

• —Quarterly performance reviews recommended
• —Annual retraining with updated data
• —Regular bias assessments
• —Ongoing validation against current practices

Citation

@misc{diabetes-readmission-model,
  title = {Hospital Readmission Prediction Model for Diabetic Patients},
  author = {Agustin, Jonathan and Robertson, Zack and Vo, Lisa},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/{REPO_ID}}}
}

@misc{diabetes-dataset,
  title = {Diabetes 130-US Hospitals for Years 1999-2008 Data Set},
  author = {Strack, B. and DeShazo, J. and Gennings, C. and Olmo, J. and
            Ventura, S. and Cios, K. and Clore, J.},
  year = {2014},
  publisher = {UCI Machine Learning Repository},
  doi = {10.24432/C5230J}
}

Model Card Authors

Jonathan Agustin, Zack Robertson, Lisa Vo

For Questions, Issues, or Feedback

• —GitHub Issues: Repository Issues
• —Email: [team contact information]

Updates and Versions

• —{pd.Timestamp.now().strftime('%Y-%m-%d')}: Initial model release
• —Feature engineering pipeline implemented
• —Comprehensive preprocessing system added
• —Model evaluation and selection completed

Last updated: {pd.Timestamp.now().strftime('%Y-%m-%d')}