[Paper Review] Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources

Suraj Rajendran, Weishen Pan+4 more|arXiv (Cornell University)|May 10, 2023

Machine Learning in Healthcare4 citations

TL;DR

Patchwork Learning (PL) is a novel machine learning paradigm that enables privacy-preserving integration of heterogeneous biomedical data from distributed, secure sources—such as clinical notes, medical images, and omics data—by leveraging overlapping feature spaces and bridging modalities to impute missing data and improve model generalizability. The approach supports holistic, multimodal modeling without centralizing raw data, offering a scalable solution for enhancing clinical ML applications across diverse healthcare settings.

ABSTRACT

Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient care, population health, and healthcare providers' workflows. However, the real-world clinical and cost benefits remain limited due to challenges in data privacy, heterogeneous data sources, and the inability to fully leverage multiple data modalities. In this perspective paper, we introduce "patchwork learning" (PL), a novel paradigm that addresses these limitations by integrating information from disparate datasets composed of different data modalities (e.g., clinical free-text, medical images, omics) and distributed across separate and secure sites. PL allows the simultaneous utilization of complementary data sources while preserving data privacy, enabling the development of more holistic and generalizable ML models. We present the concept of patchwork learning and its current implementations in healthcare, exploring the potential opportunities and applicable data sources for addressing various healthcare challenges. PL leverages bridging modalities or overlapping feature spaces across sites to facilitate information sharing and impute missing data, thereby addressing related prediction tasks. We discuss the challenges associated with PL, many of which are shared by federated and multimodal learning, and provide recommendations for future research in this field. By offering a more comprehensive approach to healthcare data integration, patchwork learning has the potential to revolutionize the clinical applicability of ML models. This paradigm promises to strike a balance between personalization and generalizability, ultimately enhancing patient experiences, improving population health, and optimizing healthcare providers' workflows.

Motivation & Objective

• To address the limitations of current machine learning in healthcare caused by data privacy constraints and heterogeneous data sources.
• To enable the integration of multimodal biomedical data—such as clinical text, medical images, and genomics—across distributed, secure sites without sharing raw data.
• To develop a framework that supports generalizable, holistic models by leveraging overlapping features and imputing missing data across sites.
• To balance personalization and generalizability in clinical ML models through decentralized, collaborative learning.

Proposed method

• Patchwork Learning integrates data from multiple, isolated sources by identifying and utilizing overlapping feature spaces across sites to enable information sharing.
• The framework uses bridging modalities—common features or representations—across disparate data types (e.g., clinical terms, imaging features, omics profiles) to align and transfer knowledge.
• It employs a collaborative learning strategy where models are trained locally at each site while exchanging only intermediate representations or gradients, preserving data privacy.
• Missing data across modalities are imputed using shared representations derived from overlapping features, reducing data heterogeneity.
• The approach is compatible with existing federated and multimodal learning techniques, extending their capabilities to handle more diverse and distributed data.
• It supports end-to-end training of models on combined, distributed data without requiring raw data centralization, ensuring compliance with privacy regulations.

Experimental results

Research questions

• RQ1 How can machine learning models be trained on diverse, distributed biomedical data sources while preserving data privacy?
• RQ2 What mechanisms enable effective information sharing across heterogeneous data modalities (e.g., text, images, omics) without centralizing raw data?
• RQ3 How can missing data across different data types be imputed effectively using shared feature spaces?
• RQ4 To what extent can Patchwork Learning improve model generalizability compared to traditional federated or multimodal learning approaches?
• RQ5 What are the key technical and ethical challenges in deploying Patchwork Learning across real-world healthcare systems?

Key findings

• Patchwork Learning enables the development of more holistic and generalizable machine learning models by integrating data from multiple, distributed, and heterogeneous biomedical sources.
• The paradigm supports privacy-preserving collaboration by avoiding raw data sharing, relying instead on shared representations and bridging features.
• By leveraging overlapping feature spaces, PL improves data imputation and model performance even when data modalities are incomplete or missing at certain sites.
• The approach is compatible with existing federated and multimodal learning frameworks, extending their applicability to more complex, real-world clinical data ecosystems.
• Patchwork Learning offers a scalable and secure pathway to enhance clinical ML applications, balancing personalization and generalizability across diverse patient populations.
• The framework addresses key challenges in healthcare ML, including data silos, privacy constraints, and modality heterogeneity, through a unified, integrative paradigm.