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.
Patent application filed by Roche Diagnostics GmbH, F. Hoffmann-La Roche AG.
Yunlong Jiao
Applied Machine Learning Research
My research interests include Deep Generative Models, Vision Language Models, Natural Language Processing, and Computational Biology.