This Jupyter notebook in Python provides a solution to the problem of predicting the likelihood of hospital readmission for patients, as required by the Hospital Readmissions Reduction Program (HRRP). The HRRP requires the Center for Medicare and Medicaid Services (CMS) to reduce payments to Inpatient Prospective Payment System (IPPS) hospitals with excess readmissions for certain medical conditions. A readmission is defined as a patient returning to the hospital within 30 days of discharge for the same reason of the initial admission, which is an indication of poor quality of initial care or care coordination following discharge from the hospital.
To address this problem, health organizations nationwide are developing protocols to limit unplanned 30-day readmissions in efforts to provide high-quality care and maintain financial viability. Efforts generally involve establishing a protocol for identifying patients currently within the hospital who are at high risk for 30-day readmissions and then proactively creating a post-discharge care plan in attempts to prevent the readmission from occurring.
This notebook provides a complete data science pipeline to solve the problem of predicting the likelihood of hospital readmission for patients, which can be used by health organizations to identify patients at high risk for 30-day readmissions and create a post-discharge care plan to prevent the readmission from occurring. The pipeline includes the following steps:
Data loading and exploration: The notebook begins by importing the necessary libraries and loading the hospital dataset. The dataset is then explored to gain insights into the data and identify any missing values or outliers.
Data preprocessing: The data is then preprocessed, which includes handling missing values, converting categorical features to numerical, and scaling the features.
Model training and evaluation: Several machine learning models are trained on the preprocessed data, including Logistic Regression, K-Nearest Neighbors, Random Forest, and XGBoost. The performance of each model is evaluated using various metrics such as accuracy, precision, recall, and F1-score.
Model selection and tuning: After comparing the performance of the different models, the XGBoost model is selected as the best model, and its hyperparameters are fine-tuned to improve its performance.
Prediction and deployment: The final XGBoost model is then used to predict the likelihood of hospital readmission for new patients based on their demographic, clinical, and utilization information. The model can be deployed by health organizations to identify patients who are at high risk for readmission and take proactive measures to prevent it from happening.
In summary, this notebook provides a solution to the problem of predicting the likelihood of hospital readmission for patients, as required by the Hospital Readmissions Reduction Program (HRRP). The pipeline includes data loading and exploration, data preprocessing, model training and evaluation, model selection and tuning, and prediction and deployment. The final model can be used by health organizations to identify patients who are at high risk for readmission and take proactive measures to prevent it from happening, in line with the efforts to provide high-quality care and maintain financial viability.