System and method for intelligent quality control of a process

A computer-implemented method for detecting in real time errors in a process, the method comprising the following steps:

• collecting through a moving window a set of data elements from the process;
• scaling the data elements via a plurality of scaling techniques to generate scaled data elements;
• basing inputs to nodes of a neural network on the scaled data elements;
• generating an output from the neural network based on the inputs, the output indicative of whether an error in the process has occurred;
• and
• repeating the above steps after moving the window to replace a data element in the window with another data element outside the window.

A method for detecting in real time errors caused by a laboratory instrument in the analysis of patient data, the method comprising the following steps:

• collecting through a moving window a number of test results from a number of patients, the test results generated by a laboratory instrument from analysis of patient specimens;
• applying the test results as inputs to nodes of a neural network;
• generating an output from the neural network based on the inputs during operation of the laboratory instrument, the output indicative of whether an error in the analysis of patient specimens has occurred;
• and
• repeating the above steps after moving the window to replace a test result in the window with another test result outside the window.

A system for detecting errors in the analysis of patient data, comprising:

• a central processing unit;
• a memory device coupled to the central processing unit and including:
• a first data structure for collecting through a moving window a number of test results from a number of patients;
• a second data structure for applying the test results as inputs to nodes of a neural network;
• a neural network for generating an output based on the inputs applied to its nodes, the output indicative of whether an error in the analysis of patient specimens has occurred;
• and
• a third data structure for moving the window to replace a test result in the window with another test result outside the window,
• the neural network generating additional outputs as the test results in the window change;
• wherein the second data structure includes a counter for counting the number of test results having values within predetermined intervals of the range of values and applying the counts of test results as inputs to the nodes of the neural network, each count being applied to a node representing the predetermined interval corresponding to the count.

A method of detecting, in real-time, errors in data produced by a laboratory instrument in analysis of patient specimens, the method comprising:

• collecting through a moving window a plurality of test results from a plurality of patients, the test results generated by the laboratory instrument from the analysis of the patient specimens;
• scaling the test results via a plurality of scaling techniques to generate scaled test results;
• counting a number of scaled test results occurring in a plurality of ranges to generate counts of the number of scaled test results occurring in the ranges;
• applying at least the counts to one or more neural networks of a first type to generate an output indicative of whether a bias or precision error is present;
• and
• via one or more neural networks of a second type, predicting a measured analyte value for a patient from other analyte values for the patent to generate an output indicative of whether a spontaneous error is present.

A computer-implemented method for detecting, in real time, a spontaneous error in a set of a plurality of measurements of analytes for a patient, the method comprising:

• collecting the set of the plurality of measurements of analytes for the patent;
• predicting a measured analyte value out of the set from other analyte values in the set;
• and
• via the predicted measured analyte value, determining whether a spontaneous error has occurred.