首页|University of Leuven (KU Leuven) Reports Findings in Machine Learning (lab2clean : a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses)
University of Leuven (KU Leuven) Reports Findings in Machine Learning (lab2clean : a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject o f a report. According to news reporting out of Leuven, Belgium, by NewsRx editor s, research stated, “The integrity of clinical research and machine learning mod els in healthcare heavily relies on the quality of underlying clinical laborator y data. However the preprocessing of this data to ensure its reliability and ac curacy remains a significant challenge due to variations in data recording and r eporting standards.” Our news journalists obtained a quote from the research from the University of L euven (KU Leuven), “We developed lab2clean, a novel algorithm aimed at automatin g and standardizing the cleaning of retrospective clinical laboratory results da ta. lab2clean was implemented as two R functions specifically designed to enhanc e data conformance and plausibility by standardizing result formats and validati ng result values. The functionality and performance of the algorithm were evalua ted using two extensive electronic medical record (EMR) databases, encompassing various clinical settings. lab2clean effectively reduced the variability of labo ratory results and identified potentially erroneous records. Upon deployment, it demonstrated effective and fast standardization and validation of substantial l aboratory data records. The evaluation highlighted significant improvements in t he conformance and plausibility of lab results, confirming the algorithm’s effic acy in handling large-scale data sets. lab2clean addresses the challenge of prep rocessing and cleaning clinical laboratory data, a critical step in ensuring hig h-quality data for research outcomes. It offers a straightforward, efficient too l for researchers, improving the quality of clinical laboratory data, a major po rtion of healthcare data. Thereby, enhancing the reliability and reproducibility of clinical research outcomes and clinical machine learning models.”
LeuvenBelgiumEuropeAlgorithmsCli nical ResearchCyborgsEmerging TechnologiesHealth and MedicineMachine Lea rning
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