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--- |
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pipeline_tag: tabular-classification |
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language: en |
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license: mit |
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model-index: |
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- name: aai540-group3/diabetes-readmission |
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results: |
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- task: |
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type: binary-classification |
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dataset: |
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name: Diabetes 130-US Hospitals |
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type: hospital-readmission |
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metrics: |
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- type: accuracy |
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value: 0.8865474882652552 |
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name: accuracy |
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- type: auc |
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value: 0.6467403398083669 |
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name: auc |
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--- |
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# aai540-group3/diabetes-readmission |
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## Model Description |
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This model predicts 30-day hospital readmissions for diabetic patients using historical patient data |
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and machine learning techniques. The model aims to identify high-risk individuals enabling targeted |
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interventions and improved healthcare resource allocation. |
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## Overview |
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- **Task:** Binary Classification (Hospital Readmission Prediction) |
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- **Model Type:** autogluon |
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- **Framework:** Python Autogluon |
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- **License:** MIT |
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- **Last Updated:** 2024-10-29 |
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## Performance Metrics |
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- **Test Accuracy:** 0.8865 |
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- **Test ROC-AUC:** 0.6467 |
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## Feature Importance |
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Significant features and their importance scores: |
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| Feature | Importance | p-value | 99% CI | |
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|---------|------------|----------|----------| |
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| 0 | 0.0563 | 3.24e-04 | [0.0294, 0.0832] | |
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| 1 | 0.0358 | 8.45e-06 | [0.0290, 0.0426] | |
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| 2 | 0.0080 | 0.0083 | [-0.0013, 0.0173] | |
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| 3 | 0.0046 | 1.96e-04 | [0.0027, 0.0065] | |
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| 4 | 0.0023 | 0.0055 | [-0.0001, 0.0046] | |
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| 5 | 0.0008 | 0.1840 | [-0.0027, 0.0043] | |
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*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.* |
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## Features |
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### Numeric Features |
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- Patient demographics (age) |
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- Hospital stay metrics (time_in_hospital, num_procedures, num_lab_procedures) |
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- Medication metrics (num_medications, total_medications) |
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- Service utilization (number_outpatient, number_emergency, number_inpatient) |
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- Diagnostic information (number_diagnoses) |
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### Binary Features |
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- Patient characteristics (gender) |
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- Medication flags (diabetesmed, change, insulin_with_oral) |
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### Interaction Features |
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- Time-based interactions (medications × time, procedures × time) |
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- Complexity indicators (age × diagnoses, medications × procedures) |
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- Resource utilization (lab procedures × time, medications × changes) |
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### Ratio Features |
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- Resource efficiency (procedure/medication ratio, lab/procedure ratio) |
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- Diagnostic density (diagnosis/procedure ratio) |
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## Intended Use |
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This model is designed for healthcare professionals to assess the risk of 30-day readmission |
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for diabetic patients. It should be used as a supportive tool in conjunction with clinical judgment. |
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### Primary Intended Uses |
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- Predict likelihood of 30-day hospital readmission |
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- Support resource allocation and intervention planning |
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- Aid in identifying high-risk patients |
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- Assist in care management decision-making |
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### Out-of-Scope Uses |
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- Non-diabetic patient populations |
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- Predicting readmissions beyond 30 days |
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- Making final decisions without clinical oversight |
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- Use as sole determinant for patient care decisions |
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- Emergency or critical care decision-making |
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## Training Data |
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The model was trained on the [Diabetes 130-US Hospitals Dataset](https://doi.org/10.24432/C5230J) |
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(1999-2008) from UCI ML Repository. This dataset includes: |
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- Over 100,000 hospital admissions |
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- 50+ features including patient demographics, diagnoses, procedures |
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- Binary outcome: readmission within 30 days |
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- Comprehensive medication tracking |
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- Detailed hospital utilization metrics |
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## Training Procedure |
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### Data Preprocessing |
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- Missing value imputation using mean/mode |
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- Outlier handling using 5-sigma clipping |
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- Feature scaling using StandardScaler |
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- Categorical encoding using one-hot encoding |
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- Log transformation for skewed features |
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### Feature Engineering |
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- Created interaction terms between key variables |
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- Generated resource utilization ratios |
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- Aggregated medication usage metrics |
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- Developed time-based interaction features |
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- Constructed diagnostic density metrics |
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### Model Training |
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- Data split: 70% training, 15% validation, 15% test |
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- Cross-validation for model selection |
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- Hyperparameter optimization via grid search |
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- Early stopping to prevent overfitting |
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- Model selection based on ROC-AUC performance |
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## Limitations & Biases |
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### Known Limitations |
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- Model performance depends on data quality and completeness |
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- Limited to the scope of training data timeframe (1999-2008) |
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- May not generalize to significantly different healthcare systems |
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- Requires standardized input data format |
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### Potential Biases |
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- May exhibit demographic biases present in training data |
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- Performance may vary across different hospital systems |
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- Could be influenced by regional healthcare practices |
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- Might show temporal biases due to historical data |
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### Recommendations |
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- Regular model monitoring and retraining |
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- Careful validation in new deployment contexts |
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- Assessment of performance across demographic groups |
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- Integration with existing clinical workflows |
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## Monitoring & Maintenance |
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### Monitoring Requirements |
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- Track prediction accuracy across different patient groups |
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- Monitor input data distribution shifts |
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- Assess feature importance stability |
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- Evaluate performance metrics over time |
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### Maintenance Schedule |
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- Quarterly performance reviews recommended |
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- Annual retraining with updated data |
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- Regular bias assessments |
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- Ongoing validation against current practices |
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## Citation |
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```bibtex |
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@misc{diabetes-readmission-model, |
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title = {Hospital Readmission Prediction Model for Diabetic Patients}, |
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author = {Agustin, Jonathan and Robertson, Zack and Vo, Lisa}, |
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year = {2024}, |
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publisher = {Hugging Face}, |
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howpublished = {\url{https://huggingface.co/{REPO_ID}}} |
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} |
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@misc{diabetes-dataset, |
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title = {Diabetes 130-US Hospitals for Years 1999-2008 Data Set}, |
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author = {Strack, B. and DeShazo, J. and Gennings, C. and Olmo, J. and |
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Ventura, S. and Cios, K. and Clore, J.}, |
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year = {2014}, |
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publisher = {UCI Machine Learning Repository}, |
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doi = {10.24432/C5230J} |
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} |
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``` |
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## Model Card Authors |
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Jonathan Agustin, Zack Robertson, Lisa Vo |
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## For Questions, Issues, or Feedback |
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- GitHub Issues: [Repository Issues](https://github.com/aai540-group3/diabetes-readmission/issues) |
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- Email: [team contact information] |
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## Updates and Versions |
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- {pd.Timestamp.now().strftime('%Y-%m-%d')}: Initial model release |
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- Feature engineering pipeline implemented |
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- Comprehensive preprocessing system added |
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- Model evaluation and selection completed |
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--- |
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Last updated: {pd.Timestamp.now().strftime('%Y-%m-%d')} |
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