Multivariate anomaly detection models enhance identification of errors in routine clinical chemistry testing

Conventional autoverification rules evaluate analytes independently, potentially missing unusual patterns of results indicative of errors such as serum contamination by collection tube additives. This study assessed whether multivariate anomaly detection algorithms could enhance the detection of suc...

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