Present Condition and Future Outlook on the Application of Machine Learning and Big Data Analytics in Multimorbidity Research

Abstract

Multimorbidity, the coexistence of two or more chronic conditions in an individual, presents complex challenges for healthcare management and research. Traditional epidemiological and statistical methods often fail to capture the intricate interactions among diseases, functional impairments, and patient outcomes. Recent advances in machine learning (ML) and big data (BD) analytics offer promising strategies to address these challenges by enabling patient phenotyping, risk stratification, and prediction of disease progression using multimodal datasets, including electronic health records. Techniques such as clustering, deep learning, reinforcement learning, and temporal modeling facilitate the identification of disease patterns, trajectories, and treatment optimization for individuals with multimorbidity. Despite these advances, practical implementation in clinical settings is limited by data heterogeneity, methodological variability, and insufficient integration of domain expertise. Collaboration among clinicians, data scientists, and AI specialists is essential to develop standardized, validated research protocols and to translate ML/BD insights into actionable clinical strategies. Ultimately, the integration of these technologies promises to improve personalized care, population health management, and the creation of multidisciplinary guidelines for the management of multimorbidity.