The Use of Machine Learning in Real-World Data: A Systematic Review of Disease Prediction and Management (Preprint)

Опубликована: Ноя. 17, 2024

Machine learning (ML) and big data analytics are revolutionizing healthcare, particularly in disease prediction, management, personalized care. With vast amounts of real-world (RWD) from sources like electronic health records (EHRs), patient registries, wearable devices, ML offers significant potential to improve clinical outcomes. However, quality, transparency, integration challenges remain. This study aims systematically review the use for prediction identifying most common methods, types, designs, evidence (RWE). A systematic followed PRISMA guidelines identify studies that utilized machine methods analyzing management. The focused on extracting related algorithms used, categories, types studies, RWE, such as devices. revealed frequently employed were Random Forest (RF), Logistic Regression (LR), Support Vector (SVM). These applied across various with cardiovascular diseases, cancers, neurological disorders being common. Real-world primarily originated EHRs, a predominant focus predictive modeling hold promise enhancing healthcare through better model interpretability, generalizability must be addressed integrate models fully into practice.

Язык: Английский

The Use of Machine Learning in Real-World Data: A Systematic Review of Disease Prediction and Management (Preprint)

JMIR Medical Informatics, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 17, 2024

Язык: Английский