Predicting disease outcomes from remote monitoring using machine learning: a systematic review

Abstract Background Chronic conditions cause millions of deaths annually worldwide. Remote patient monitoring using wearable devices and sensors, combined with machine learning (ML), offers promising strategies for disease management. However, diverse methodological approaches and study designs impede comparability and the development of best practice guidelines. Methods A systematic review was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Four scientific databases were searched for relevant prospective studies published between 2014 and 2024. Studies had to use ML to predict disease outcomes of chronic conditions in remotely monitored patients. The studies were tagged for characteristics such as health outcomes, dataset, monitored parameters, and algorithms. Results From 6668 initially identified studies, 76 met inclusion criteria. 73.7% of studies were considered to have a high risk of bias, mainly due to methodological shortcomings in the Analysis domain. Parkinson’s disease was most frequently monitored, followed by diabetes and chronic obstructive pulmonary disease (COPD). Wearable devices were the predominant remote sensors, with accelerometer data being the most common parameter. Tree-based algorithms were most frequent, and studies using leave-one-out cross-validation showed significantly higher accuracy. Feature engineering and publication year were also significantly associated with model performance. Conclusion This review highlights both progress and challenges in applying ML to chronic disease monitoring. While conditions like Parkinson’s, COPD, and diabetes are well-represented, others such as liver and kidney diseases are underexplored. Future research should prioritize standardization of methodologies, model interpretability, and ethical considerations including data privacy and algorithmic fairness. When properly implemented, ML-driven remote monitoring has the potential to enhance patient care, reduce complications, and deepen our understanding of chronic conditions. However, addressing challenges in reproducibility, generalizability, and clinical integration is crucial for advancing the field.