Machine Learning Across Heterogeneous Biomedical Data: Representation, Integration, and Deployable Systems

Biomedical prediction increasingly requires machine learning methods capable of integrating heterogeneous data spanning molecular, physiological, clinical, behavioral, and environmental representations. Yet progress in this area has often been framed primarily in terms of predictive algorithms, with less attention placed on how biomedical data are represented, integrated, and translated into deployable systems. This review synthesizes machine learning for heterogeneous and multimodal biomedical data settings through three complementary perspectives. First, we organize the literature into four recurring prediction regimes-structured biomedical prediction, high-dimensional biomedical signals, multimodal learning, and temporal or longitudinal modeling-and review representative datasets and model families associated with each. Second, we analyze recurrent pipeline architecture motifs, highlighting trends in handcrafted feature pipelines, learned representations, staged predictive systems, and robustness-aware architectures. Third, we examine biomedical machine learning as a constrained systems design problem shaped by partial observability, alignment challenges, robustness, and deployment requirements. Across regimes, a central theme emerges: effective biomedical machine learning depends not only on model choice, but also on principled design of representations, information flow, modularity, and deployability. The reviewed approaches emphasize multimodal integration and predictive learning rather than explicit mechanistic scale-coupling models.