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. For any given laboratory test Y, an "optimized and personalized reference population" was defined by keeping only patients whose laboratory values for all Y-correlated tests fell within their own usual reference intervals, and by partitioning groups by individual-specific variables like sex and age category. The method was applied to platelet count. RESULTS: Laboratory data were recorded for 28,082 individuals. At the end of the algorithmic process, seven correlated laboratory tests were chosen, resulting in a reference sample of 159 platelet counts. A new 95 % reference interval was constructed [152-334 × 109/L], notably reduced (27.2 %) compared to conventional reference values [150-400 × 109/L]. The reference interval was validated on a sample of 2,129 patients from another downtown laboratory, emphasizing the potential transference of the hospital-derived reference limits. CONCLUSION: This method offers new perspectives in laboratory data interpretation, especially in patient screening and longitudinal follow-up.

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. Inspired by state-of-the-art manifold learning and hull estimations algorithms, we propose a novel framework, the datascape, that leverages topology and graph theory to abstract heterogeneous datasets. Built upon the combination of a nearest neighbor graph, a set of convex hulls, and a metric distance that respects the shape of the data, the datascape allows exploration of the dataset's underlying space. We show that the datascape can uncover underlying functions from simulated datasets, build predictive algorithms with performance close to state-of-the-art algorithms, and reveal insightful geodesic paths between points. It demonstrates versatility through ecological, medical, and simulated data use cases.