Journal
CLINICS IN LABORATORY MEDICINE
Volume 40, Issue 1, Pages 51-+Publisher
W B SAUNDERS CO-ELSEVIER INC
DOI: 10.1016/j.cll.2019.11.009
Keywords
Big Data; Laboratory medicine; Machine learning; Direct-to-consumer testing; DTC; Harmonization
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The recent increase in accessible medical and clinical laboratory Big Data has led to a corresponding increase in the use of machine-learning tools to develop integrative diagnostic models incorporating both existing and new test data. The rise of direct-to-consumer (DTC) testing paradigms raises the possibility of predictive models that use these new sources. This article discusses several distinct challenges raised by the DTC approach, including issues of centralized data collection, ascertainment bias, linkage to medical outcomes, and standardization/harmonization of results. Several solutions to maximize the promise of machine learning data analytics for DTC data are suggested.
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