ArticlePeerJ. Computer science2023
lab: an R package for generating analysis-ready data from laboratory records.
Article in PeerJ. Computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
Background: Electronic health records (EHRs) play a crucial role in healthcare decision-making by giving physicians insights into disease progression and suitable treatment options. Within EHRs, laboratory test results are frequently utilized for predicting disease progression. However, processing laboratory test results often poses challenges due to variations in units and formats. In addition, leveraging the temporal information in EHRs can improve outcomes, prognoses, and diagnosis predication. Nevertheless, the irregular frequency of the data in these records necessitates data preprocessing, which can add complexity to time-series analyses. Methods: To address these challenges, we developed an open-source R package that facilitates the extraction of temporal information from laboratory records. The proposed Results: Using the Conclusions: The proposed
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