基于三维子轨迹聚类算法的临床路径挖掘方法OACSTPCD

Aiming at the problem that the formulation and implementation of clinical pathways are affected by the hospital's own conditions and the complexity of diseases,we propose a clinical pathway mining method based on the three-dimensional sub-trajectory clustering algorithm.According to the characteristics of clinical diagnosis and treatment process data with regularity and temporal sequence,the proposed method first converts a large amount of clinical diagnosis and treatment process data into three-dimensional time-sequential trajectories that approximate the flight trajectories,and adjusts the distribution of trajectories using time-weighted methods.Second,based on the traditional TRACLUS algorithm,K-dimension tree is used to accelerate the optimization of neighborhood search,and the similarity measure in Hilbert space is introduced to make it adaptable to high-dimensional trajectory clustering.Finally,by clustering and analyzing a large number of trajectories,a typical clinical diagnosis and treatment process is extracted from them,and then a clinical pathway is obtained for actual implementation.A series of experiments were conducted on the clinical data of neonatal hypoglycemia in a tertiary hospital,and the results confirmed that the proposed method can extract the clinical pathways actually implemented in various local situations,which can help doctors formulate a more personalized treatment plan,and the results of the experi-ments provide a direction and a strong basis for the improvement and implementation of the standard clinical pathways for neonatal hypo-glycemia.