Failure State Prediction for Automated Analyzers for Analyzing a Biological Sample

Abstract

A method for predicting a failure state of an automated analyzer for analyzing a biological sample is disclosed. The method includes obtaining a prediction algorithm for predicting a failure state of an automated analyzer. The prediction algorithm is configured to predict a failure state of the automated analyzer based on calibration data and/or quality control data generated by an automated analyzer. The method also includes obtaining calibration data and/or quality control data of the automated analyzer and processing the calibration data and/or quality control data by using the prediction algorithm to predict a failure state of the automated analyzer.

Publication
US Patent

Patent application filed by Roche Diagnostics GmbH, F. Hoffmann-La Roche AG.

Yunlong Jiao
Yunlong Jiao
Machine Learning Scientist

My research interests include Deep Generative Models, Representation Learning, Natural Language Processing, Neural Text-to-Speech, and Gaussian Processes.

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