Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Rd, Shanghai 200032, China.
Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Rd, Shanghai 200032, China.
Clin Chim Acta. 2024 Feb 1;554:117774. doi: 10.1016/j.cca.2024.117774. Epub 2024 Jan 12.
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.
基于患者的实时质量控制（PBRTQC）模型必须针对不同的临床实验室进行优化，但最近研究中探索的网格搜索（GS）算法在这方面效率不高。因此，寻找一种有效的优化算法对于未来的研究和 PBRTQC 的实施至关重要。
我们比较了五种常用的优化算法的效率和性能，包括 GS、模拟退火（SA）、遗传算法（GA）、差分进化（DE）和粒子群优化（PSO），以优化常规 PBRTQC 和回归调整实时质量控制（RARTQC）模型，用于血清丙氨酸氨基转移酶和钠。
GS、GA、DE 和 PSO 为模型提供了相似的性能。然而，GA 和 DE 所需的计算时间明显少于 GS。结果还表明，优化方法发现最佳值的机会和所需的计算时间之间存在一般权衡。
在建立 PBRTQC 或 RARTQC 模型时，应采用更有效的优化方法，以节省时间和计算能力，从而能够开发更复杂的模型，并提高广泛的 PBRTQC 应用的可扩展性。
