Care Machine Learning Integration of Electronic Health Records and Multiomics for Population-Level Health Monitoring

Abstract

This paper examines how machine learning methods can be used to integrate Electronic Health Records (EHR) and multiomics data (including genomics, proteomics, and metabolomics) to monitor the health of a population at a population level. It is hoped to improve the forecasting of disease development, therapy reactions, and general well-being by incorporating clinical information in EHRs with multiomics molecular understanding. The comprehensive data collection process comprised EHR and multiomics data, which would be compatible due to the methods of normalizing and preprocessing data. Different machine learning models (supervised as well, such as Random Forest or Gradient Boosting Machines (GBM)) were employed to make predictive models, whereas unsupervised learning algorithms, such as k-means clustering, were employed to determine patient subgroups. These findings indicated a highly significant enhancement in accuracy (87%), precision (0.85), recall (0.82), and F1-score (0.83) to show how this combined method can be used to improve predictive healthcare. The combination of multiomics and machine learning models was more insightful and predictive in comparison to traditional approaches that use EHR data as the main source of information. Irrespective of these developments, there are problems like data quality, model interpretability, and data integration. These issues should be tackled in order to maximize the advantages of incorporating EHR and multiomics information into the healthcare mainstream routine. In future studies, refinement of these strategies, the accessibility and standardization of multiomics data, and better interpretability of machine learning models should also be investigated to make them reliable in clinical use