Cloud-Enabled Time-Series Forecasting for Hospital Readmissions Using Transformer Models and Attention Mechanisms

Bhagath Singh Jayaprakasam; Thanjaivadivel M

Authors

Bhagath Singh Jayaprakasam Author
Thanjaivadivel M Author

Keywords:

Cloud-Enabled, Time-Series Forecasting, Hospital Readmissions, Transformer Models, Attention Mechanisms, Machine Learning, Healthcare Data, Predictive Modeling, Sequential Data, Patient Data, Readmission Risk, Real-Time Processing, Feature Extraction, Healthcare Predictions, Deep Learning

Abstract

Hospital readmissions are one of the biggest issues in healthcare. They increase the cost and
worsen patient outcomes, putting additional strain on already-over-stretched resources. Traditional
models for prediction often fail to handle the complexity and high dimensionality of healthcare
data. This paper outlines a solution: an approach to the predictive model for hospital readmission
using transformer models with attention mechanisms deployed on cloud infrastructure. This
approach allows scalability and real-time processing, providing a healthcare professional with
more accurate, actionable insights that reduce unnecessary hospitalizations and help in enhancing
patient outcomes. The methodology allows the processing of complex time-series data, like vital
signs, lab results, and treatment history, thus making the model focus on key features and time
periods for improving predictions. The cloud-based infrastructure lets the model scale and adapt
dynamically to new data. The experimental results reveal that the proposed model outperforms
traditional methods, achieving 88% accuracy, 85% precision, 83% recall, 84% F1-score, and 92%
AUC. Future improvements may include integration of genomic data and multi-center deployment.