Predicting Hospital Readmission Rates Using Machine Learning

Introduction

Predicting hospital readmission rates is a critical task in healthcare management, aimed at improving patient outcomes and reducing healthcare costs. This project proposal focuses on developing a machine learning model to predict the likelihood of patient readmission within 30 days of discharge, utilizing patient data and hospital records.

Background

Hospital readmissions are costly and often preventable. Recent studies have demonstrated that machine learning algorithms can effectively predict readmission risks by analyzing large datasets of patient information. These models leverage various features such as patient demographics, medical history, and treatment details to provide accurate predictions.

Project Objective

The primary objective of this project is to develop a predictive model that accurately forecasts hospital readmissions. The model will assist healthcare providers in identifying high-risk patients and implementing targeted interventions to reduce readmission rates.

Methodology

1. Data Collection and Preprocessing

• Datasets: Utilize publicly available datasets such as the Hospital Readmissions Reduction Program (HRRP) dataset from Medicare or other relevant healthcare databases.
• Feature Selection: Identify key features such as age, gender, comorbidities, length of stay, discharge instructions, and previous admission history.

2. Model Development

• Algorithm Selection: Explore various machine learning algorithms including logistic regression, decision trees, random forests, and gradient boosting machines.
• Model Training: Split the data into training and testing sets to evaluate the model's performance using cross-validation techniques.

3. Evaluation

• Performance Metrics: Assess the model using metrics like accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve.
• Model Optimization: Fine-tune hyperparameters to enhance model performance and validate results using an independent test set.

Expected Outcomes

The proposed machine learning model is expected to improve the prediction of hospital readmission rates compared to traditional methods. By accurately identifying patients at risk of readmission, healthcare providers can implement preventive measures to enhance patient care and reduce unnecessary costs.

Conclusion

This project aims to contribute to the field of healthcare analytics by developing a robust predictive model for hospital readmissions. Leveraging machine learning techniques will enable more proactive management of patient care and resource allocation in hospitals.

For further details on related research, please refer to the paper "Predicting Hospital Readmission Rates Using Machine Learning," available at https://www.sciencedirect.com/science/article/pii/S1877050920307675.

Dataset Link: Hospital Readmissions Reduction Program (HRRP) dataset