The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review.

ABSTRACT:

Background

Machine learning (ML) and big data analytics are rapidly transforming health care, particularly disease prediction, management, and personalized care. With the increasing availability of real-world data (RWD) from diverse sources, such as electronic health records (EHRs), patient registries, and wearable devices, ML techniques present substantial potential to enhance clinical outcomes. Despite this promise, challenges such as data quality, model transparency, generalizability, and integration into clinical practice persist.

Objective

This systematic review aims to examine the use of ML for analyzing RWD in disease prediction and management, identifying the most commonly used ML methods, prevalent disease types, study designs, and the sources of real-world evidence

SUBMITTER: Alhumaidi NH 

PROVIDER: S-EPMC12226786 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature