Use of hospital big data to optimize and personalize laboratory test interpretation with an application.

Background And Aims: In laboratory medicine, test results are generally interpreted with 95% reference intervals but correlations between laboratory tests are usually ignored. We aimed to use hospital big data to optimize and personalize laboratory data interpretation, focusing on platelet count.

Material And Methods: Laboratory tests were extracted from the hospital database and exploited by an algorithmic stepwise procedure.

Datascape: exploring heterogeneous dataspace.

Data science is a powerful field for gaining insights, comparing, and predicting behaviors from datasets. However, the diversity of methods and hypotheses needed to abstract a dataset exhibits a lack of genericity. Moreover, the shape of a dataset, which structures its contained information and uncertainties, is rarely considered.