Abstract: Clustering for asynchronous time series of clinical laboratory indicators,and finding the patient group with similar variation trends of clinical laboratory indicators,have a very important value for the conduct of precision medicine.Taking into account the frequency of inspection and the testing time points of different patients are not fully synchronized, asynchronous time series were preprocessed to achieve the synchronization of different time dimensions and time points.On this basis,we improved the DBScan algorithm by introducing a user-defined parameter namely noises share NoisePro.Then,we proposed a LabTS-CLU time series clustering algorithm of asynchronous clinical test indicators based on density divided thoughts.Finally,experimental results on the time series of glycated hemoglobin dataset of more than 100 thousand diabetics in the past 10 years from a hospital demonstrate the effectiveness of the proposed algorithm.

Key words: Clinical indicators,Asynchronous time series,Density clustering