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

Area of Science:

• Biomedical Informatics
• Machine Learning
• Data Science

Background:

• Biomedical prediction increasingly relies on machine learning (ML) to integrate diverse data types (molecular, clinical, environmental).
• Current research often prioritizes predictive algorithms over data representation, integration, and system deployment.
• A gap exists in understanding how to effectively structure ML pipelines for heterogeneous biomedical data.

Purpose of the Study:

• To synthesize machine learning approaches for heterogeneous and multimodal biomedical data.
• To analyze common prediction regimes, pipeline architectures, and system design constraints in biomedical ML.
• To highlight the importance of data representation, information flow, and deployability in successful biomedical ML systems.

Main Methods:

• Literature review organized into four prediction regimes: structured prediction, high-dimensional signals, multimodal learning, and temporal modeling.
• Analysis of pipeline architecture motifs: handcrafted features, learned representations, staged systems, and robustness-aware designs.
• Examination of biomedical ML as a constrained systems design problem, considering observability, alignment, robustness, and deployment.

Main Results:

• Identified four key prediction regimes and associated datasets/models in biomedical ML.
• Highlighted trends in pipeline architectures, including learned representations and staged systems.
• Emphasized that effective biomedical ML hinges on principled design of representations, information flow, modularity, and deployability, beyond just algorithm choice.

Conclusions:

• Successful machine learning for biomedical prediction requires a holistic approach, integrating data representation and system design with algorithm selection.
• Multimodal data integration and predictive learning are central themes, often prioritized over explicit mechanistic models.
• Future advancements depend on addressing challenges in data alignment, robustness, and deployment requirements for real-world applications.