Transforming Medical Practice: Harnessing the Power of Big Data and Machine Learning for Predictive Precision Medicine

Abstract

Machine learning (ML) techniques have been widely applied in precision medicine for the early identification of high-risk individuals using Electronic Health Records (EHRs). However, real-world EHR data often contain missing values and inconsistent patient follow-ups, creating challenges for predictive modeling. This study proposes an ensemble approach that leverages various machine learning models, such as Long Short-Term Memory (LSTM) networks, Recurrent Neural Networks (RNNs), and Random Forest (RF) models, combined through a weighted averaging scheme. To address the issue of missing data caused by irregular clinical visits, the Multivariate Imputation by Chained Equations (MICE) algorithm is used for data imputation. The ensemble model is evaluated on a real-world EHR dataset and demonstrates improved performance in predicting health outcomes. These results highlight the potential of ensemble learning and advanced imputation methods to enhance predictive accuracy, offering a robust solution for analyzing incomplete EHR data. This study provides a foundation for developing future precision medicine models that incorporate multiple clinical tests for individualized risk assessment.

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