Unsupervised techniques for identifying univariate and multivariate anomalies in scientific instrument results within a laboratory information management system

Abstract

A method of detecting sample anomalies within a laboratory information management system includes obtaining a first result for a sample, processing the first result via a univariate machine learning model, processing a plurality of results for the sample via a multivariate machine learning model in response to the univariate machine learning model generating a normal output for the first result, and flagging, within the laboratory information management system, the sample for rejection processing in response to the multivariate machine learning model generating an abnormal output for the plurality of samples. The first result represents a first type of result, the univariate machine learning model is trained using unsupervised machine learning, the plurality of results includes the first result, each of the plurality of results represents a different type of result for the sample, and the multivariate machine learning model trained using unsupervised machine learning.