Exploring optimization algorithms for establishing patient-based real-time quality control models

•Evolution Algorithms cansignificantly decrease the computational time required for hyperparameter optimization in patient-based real-time quality control models.•More efficient optimization approaches provide the groundwork for advanced PBRTQC algorithms like regression-adjusted real-time quality control.•The use of efficient optimization hyperparameters may enhance the automation of PBRTQC model construction and aid in future PBRTQC model deployment in the real world. Patient-based real-time quality control (PBRTQC) models must be optimized for use in different clinical laboratories, but the grid search (GS) algorithm explored in recent studies for this purpose is inefficient. Thus, finding an efficient optimization algorithm is critical for future research and implementation of the PBRTQC. We compared the efficiency and performance of five commonly used optimization algorithms, including GS, simulated annealing (SA), genetic algorithms (GA), differential evolution (DE), and particle swarm optimization (PSO), to optimize conventional PBRTQC and regression-adjusted real-time quality control (RARTQC) models for serum alanine aminotransferase and sodium. The GS, GA, DE, and PSO provided models with similar performances. However, GA and DE required significantly less computation time than GS. The results also demonstrate a general tradeoff between the optimization method's chance of discovering the optimum and the computation time required. More efficient optimization methods should be adopted when establishing PBRTQC or RARTQC models to save time and computing power that will enable the development of more complex models and increase the scalability of extensive PBRTQC applications.