Transforming Healthcare Through Data Analytics: Toward a Safer, Smarter, and More Secure Health System

Predicting key clinical outcomes like mortality, sepsis and a 30-day readmission is important for improving patient care and the use of clinical resources in intensive care units (ICUs). Here we present a bifurcated approach that includes both centralized and federated learning approaches to establish predictive models utilizing real world healthcare data-MIMICIV; Synthea data. In the centralized context, we modeled mortality prediction, sepsis prediction and 30-day readmission prediction using a CNN-LSTM architecture that is designed to learn complex temporal and sequential patterns of patient data. We mostly used tabular data in our implementation, but the architecture was set up to allow the model to learn temporal and sequential records. The architecture allowed a model to learn relationships among features using pseudo-sequence data. The predictive model achieved very good performance; achieving a mortality prediction accuracy of 97.45%, detection accuracy of sepsis at 98.45%, and forecasting accuracy of 30-day readmission prediction at 98.47%. These improved results outperformed previous and traditional classifiers Logistic Regression and Random Forest & SVM, supporting the consideration of deep learning solutions in clinical prediction tasks. In the federated learning context, we proposed a distributed training frame work based on LightGBM that utilizes the Synthea nodes for distributed training, while preserving confidentiality, this will allow Synthea nodes to contribute to a model at a global level while preserving patient privacy. Through the federated model, we reached a global accuracy of 98.92% in predicting hypertension, all while maintaining minimal communication overhead and strong distribution performance across the nodes. These comparisons showed that our proposed federated method could deliver a high predictive accuracy, and manage class imbalance, a topical viewpoint in the real healthcare. Finally, this research paper shows the importance of integrating intelligent deep learning systems and privacy-sensitive mechanisms. We find that these hybrid solutions can have a significant effect and add value to clinical decision support, especially in the situations where information sharing is limited due to privacy issues.