AnthonyByansi / clinical-risk-stratification

A model that can predict a patient's risk of hospital readmission within 30 days of discharge for patients with congestive heart failure (CHF)

Clinical Risk Stratification Project

The Clinical Risk Stratification Project aims to develop a model that can predict a patient's risk of hospital readmission within 30 days of discharge for patients with congestive heart failure (CHF). This can help healthcare providers intervene early and prevent adverse outcomes, leading to improved patient care and reduced healthcare costs.

Data

The data used in this project will be collected from electronic health records and other relevant sources. The dataset will include patient demographics, medical history, and other factors that may be associated with the risk of hospital readmission.

Methodology

The project will involve the following steps:

• Identify the outcome of interest: predicting the risk of hospital readmission within 30 days of discharge for patients with CHF.
• Gather and preprocess the data: collect and clean the data for analysis, dealing with missing values, outliers, and other data quality issues.
• Select features: identify the most important factors associated with the risk of hospital readmission using statistical methods and machine learning algorithms.
• Train the model: use the selected features to train a predictive model using suitable algorithms such as logistic regression, decision trees, or neural networks.
• Evaluate the model: evaluate the model's performance on a validation dataset using metrics such as accuracy, sensitivity, specificity, and AUC-ROC.
• Deploy the model: deploy the model in clinical settings to predict patients' risk of hospital readmission and intervene early to prevent adverse outcomes.

Usage

To reproduce the results of this project, follow these steps:

• Clone the repository to your local machine.
• Install the necessary dependencies using pip install -r requirements.txt.
• Run the Jupyter notebooks in the notebooks directory to preprocess the data, select features, train and evaluate the model.
• The trained model can be found in the models directory.
• The reports directory contains the figures, output and scripts used to generate the final report.

Contributors

• Byansi Anthony

License

This project is licensed under the MIT License - see the LICENSE file for details.