Considerations | Traditional PBRTQC | Next-generation PBRTQC |
---|---|---|
Underlying statistics | Moving statistics involving data distribution, such as average, median, standard deviation, percentile, and positive results. This may include statistical weighting | Regression adjustment, classification with a machine learning algorithm, AD, and hybrid statistical and machine learning methods |
Input data for statistical algorithms | Sequential patient results only | Sequential patient results, patient demographic and clinical data, and instrument-related data |
Extreme result/outlier exclusion strategy | Patient subpopulation selection, statistical exclusion, winsorization | Similar to traditional PBRTQC, but can be run without data exclusion |
Hyperparameters need to be optimized | Truncation limits, window size, and weights for weighted average methods | Machine learning algorithm hyperparameters, random forest tree size and number of trees, deep learning network structure |
Statistical parameter optimization | Optimization through simulation | Can be optimized through simulation, but using simulation may be problematic. More real-world out-of-control data are needed |
Optimization outcome | Number of patient results affected before error detection, false positive rate | Similar to traditional PBRTQC; it can also include metrics, such as the area under the receiver operating characteristic curve |
Other considerations | Relatively simple, and the parameters and output are explainable. Lower computing resource requirements | Commanding heavy computing resources, implementation is highly customized to individual laboratories |
Abbreviation: AD, anomaly detection; PBRTQC, patient-based real-time QC.
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