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Article Abstract
TriNetX, a rapidly growing global network of anonymized patient data, enables clinical researchers to perform large-scale retrospective cohort studies. However, its functionality for querying laboratory data outcomes is significantly constrained, as it only provides the results of the most recent test within a specified observation period. Consequently, the platform is not optimized for analyzing laboratory data collected at multiple time points during an observation period. This paper introduces innovative, data-informed solutions to address these limitations, offering practical guidance for researchers aiming to leverage TriNetX for examining clinical laboratory data.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12334939 | PMC |
| http://dx.doi.org/10.11613/BM.2025.030502 | DOI Listing |
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