metadata

pipeline_tag: tabular-classification
language: en
library_name: transformers
license: mit
model-index:
  - name: aai540-group3/diabetes-readmission
    results:
      - task:
          type: binary-classification
        dataset:
          name: Diabetes 130-US Hospitals
          type: hospital-readmission
        metrics:
          - type: accuracy
            value: 0.8865474882652552
            name: accuracy
          - type: auc
            value: 0.6467403398083669
            name: auc

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:

Feature	Importance	p-value	99% CI
0	0.0563	3.24e-04	[0.0294, 0.0832]
1	0.0358	8.45e-06	[0.0290, 0.0426]
2	0.0080	0.0083	[-0.0013, 0.0173]
3	0.0046	1.96e-04	[0.0027, 0.0065]
4	0.0023	0.0055	[-0.0001, 0.0046]
5	0.0008	0.1840	[-0.0027, 0.0043]

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 (time_in_hospital, num_procedures, num_lab_procedures)
Medication metrics (num_medications, total_medications)
Service utilization (number_outpatient, number_emergency, number_inpatient)
Diagnostic information (number_diagnoses)

Binary Features

Patient characteristics (gender)
Medication flags (diabetesmed, change, insulin_with_oral)

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')}