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Pac Symp Biocomput. 2019;24:439-443.

Merging heterogeneous clinical data to enable knowledge discovery.

Author information

1
Department of Biomedical Data Science, Stanford University, 1265 Welch Rd, Stanford, CA 94305, United States, martsen@stanford.edu.

Abstract

The vision of precision medicine relies on the integration of large-scale clinical, molecular and environmental datasets. Data integration may be thought of along two axes: data fusion across institutions, and data fusion across modalities. Cross-institutional data sharing that maintains semantic integrity hinges on the adoption of data standards and a push toward ontology-driven integration. The goal should be the creation of query-able data repositories spanning primary and tertiary care providers, disease registries, research organizations etc. to produce rich longitudinal datasets. Cross-modality sharing involves the integration of multiple data streams, from structured EHR data (diagnosis codes, laboratory tests) to genomics, imaging, monitors and patient-generated data including wearable devices. This integration presents unique technical, semantic, and ethical challenges; however recent work suggests that multi-modal clinical data can significantly improve the performance of phenotyping and prediction algorithms, powering knowledge discovery at the patient- and population-level.

PMID:
30864344
PMCID:
PMC6447393
[Indexed for MEDLINE]
Free PMC Article

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