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Data Integration Challenges for Machine Learning in Precision Medicine

Martínez-García, Mireya, Hernández-Lemus, Enrique·2022·Frontiers in Medicine
DOI 10.3389/fmed.2021.784455PMC PMC8821900
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Abstract

A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.

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  • Clinical adoption of multimodal XAI in oncology is constrained by limited interpretability and fragmented use of RWD.

    “Multimodal explainable artificial intelligence (XAI) is transforming oncology, yet its clinical adoption is constrained by limited interpretability and fragmented use of real-world data (RWD), including electronic medical records, geospatial data, and social determinants of health.”

    From X-CART: a multimodal real-world data pipeline for explainable AI-driven patient navigation in radiation oncology (2026) · cites this paper as [7]

    Section 5 identifies interpretability as an issue for clinical AI/ML and says that integrating electronic health records with social and environmental information remains a challenge. The Introduction also describes the heterogeneity of electronic medical records. These passages support the general barriers, but do not establish them specifically for multimodal XAI in oncology or identify geospatial data as part of fragmented RWD use. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • These challenges are compounded when data originates from heterogeneous real-world sources that were not designed with AI readiness or interoperability in mind.

    “These challenges are compounded when data originates from heterogeneous real-world sources, including routine clinical care records, community health databases, and population-level geospatial repositories that were not designed with AI readiness or interoperability in mind.7”

    From X-CART: a multimodal real-world data pipeline for explainable AI-driven patient navigation in radiation oncology (2026) · cites this paper as [7]

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Cited by

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Authors
Martínez-García, M., & Hernández-Lemus, E.
Venue
Frontiers in Medicine
Publisher
Frontiers Media SA
Published
2022-01-25
Volume
8
ISSN
2296-858X
DOI
10.3389/fmed.2021.784455
PMC
PMC8821900
Cited by
158
References
28
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • Consejo Nacional de Ciencia y Tecnología (285544)
  • Fundación Marcos Moshinsky

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