Integrating Longitudinal Electronic Health Records with Machine Learning for Personalized Cancer Management

This study proposes an innovative approach to personalized cancer management by integrating Longitudinal Electronic Health Records (LEHR) with ensemble machine learning algorithms. The method encompasses data preprocessing, feature engineering, and model development. Data preprocessing involves the critical task of cleaning and transforming raw LEHR data to ensure suitability for further analysis. Feature engineering aims to extract meaningful attributes from the preprocessed data. The heart of the approach lies in model development using ensemble learning, combining diverse base models to form a robust predictive model. The ensemble model enhances prediction accuracy, adapts to varying data types, and exhibits computational efficiency.