Failure state prediction for automated analyzers for analyzing a biological sample

An automated analyzer for analyzing a biological sample, the automated analyzer comprising:

• a detection unit to detect one or more properties of the biological sample;
• a processor;
• and
• a memory comprising a plurality of instructions stored thereon that, in response to execution by the processor, causes the automated analyzer to:
• determine a machine learning prediction algorithm for predicting a failure state of an automated analyzer based on historic analyzer data, wherein the historic analyzer data includes historic data regarding the occurrence of failure states and historic calibration data and/or quality control data, and wherein the machine learning 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;
• obtain calibration data and/or quality control data of the automated analyzer;
• and
• process the calibration data and/or quality control data by using the machine learning prediction algorithm to autonomously predict a failure state of the automated analyzer,
• wherein to determine the machine learning prediction algorithm includes to train an initial machine learning prediction algorithm using the historic data regarding the occurrence of failure states and the historic calibration data and/or quality control data,
• wherein the machine learning prediction algorithm is configured to have one output state indicating that a failure state is expected and a second output state indicating that no failure state is expected,
• wherein to determine the machine learning prediction algorithm further includes to determine a length of a reactive interval between a period of time in which the quality control and/or the calibration data is used to predict a failure event and the failure event.

One or more non-transitory machine-readable storage media comprising a plurality of instructions stored therein that, in response to execution by a processor, causes an automated analyzer to:

• determine a machine learning prediction algorithm for predicting a failure state of an automated analyzer based on historic analyzer data, wherein the historic analyzer data includes historic data regarding the occurrence of failure states and historic calibration data and/or quality control data, and wherein the machine learning 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;
• obtain calibration data and/or quality control data of the automated analyzer;
• and
• process the calibration data and/or quality control data by using the machine learning prediction algorithm to autonomously predict a failure state of the automated analyzer,
• wherein to determine the machine learning prediction algorithm includes to train an initial machine learning prediction algorithm using the historic data regarding the occurrence of failure states and the historic calibration data and/or quality control data,
• wherein the machine learning prediction algorithm is configured to have one output state indicating that a failure state is expected and a second output state indicating that no failure state is expected,
• wherein to determine the machine learning prediction algorithm further includes to determine a length of a reactive interval between a period of time in which the quality control and/or the calibration data is used to predict a failure event and the failure event.