Computer Science ›› 2016, Vol. 43 ›› Issue (9): 120-123.doi: 10.11896/j.issn.1002-137X.2016.09.023

Previous Articles     Next Articles

Efficient and Effective Clustering Algorithm for Asynchronous Time Series of Clinical Laboratory Indicators

CHEN De-hua, HAN Xue-shi, LE Jia-jin and ZHU Li-feng   

  • Online:2018-12-01 Published:2018-12-01

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

[1] Fravolini M L,Cascianelli S.Pier Giorgio Fabietti:A Learning Strategy for the Autonomous Control of Type 1 Diabetes[J].Applied Artificial Intelligence,2015,29(6):531-562
[2] Goodwin G C,Medioli A M,Carrasco D S,et al.YongjiFu:A fundamental control limitation for linear positive systems with application to Type 1 diabetes treatment[J].Automatica,2015,55:73-77
[3] Kellner D,Klappstein J,Dietmayer K.Grid-based DBSCAN for clustering extended objects in radar data[C]∥2012 IEEE Conference on Intelligent Vehicles Symposium.2012:365-370
[4] Aghabozorgi S R,WahTeh Y.Clustering of large time seriesdatasets[J].Intelligent Data Analysis,2014,18(5):793-817
[5] Li Bin,Tan Li-xiang,Zhang Jing-song.Time series symbolicmethods facing data mining[J].Journal of Circuit and Systems,2000,5(2):9-14(in Chinese) 李斌,谭立湘,章劲松.而向数据挖掘的时间序列符号化力法研究[J].电路与系统学报,2000,5(2):9-14
[6] Li Ai-guo,Qin Zheng.On-line segmentation of time-series data [J].Journal of Software,2004,5(11):1671-1679(in Chinese) 李爱国,覃征.在线分割时间序列[J].数据软件学报,2004,5(11):1671-1679
[7] Keogh E,Kasetty S.On the need for time series data miningbenchmarks:A survey and empirical demonstration[J].Data Mi-ning and Knowledge Discovery,2003,7(4):349-371
[8] Tewari G,Snyder J,Sander P V.Signal-speciallized parame-terization for piecewise linear reconstruction[C]∥Proceedings of the Eurographics Symposium on Ueometry Processing.New York,USA,2004:55-64
[9] Tseng Y J,Ping Xiao-ou,Liang J D,et al.FeipeiLai:Multiple-Time-Series Clinical Data Processing for Classification With Merging Algorithm and Statistical Measures[J].IEEE Journal of Biomedical and Health Informatics,2015,9(3):1036-1043
[10] Sitaram R,Zhang H,Guan C,et al.Temporal classification ofmulti-channel near-infrared spectroscopy signals of motor imagery for developing abrain-computer interface[J].NeuroImage,2007,34(4):1416-1427
[11] Yin Z,Zhang J.Identification of temporal variations in mental workload using locally-linear embedding based EEG feature reduction andsupport vector machine based clustering and classification techniques[J].Comput.Methods Programs Biomed., 2014,115(3):119-134

No related articles found!
Full text



No Suggested Reading articles found!