Pattern Similarity in Time Interval Sequences

被引：2

作者：

Bonomi, Luca ^{[1
]}

Jiang, Xiaoqian ^{[2
]}

机构：

[1] Univ Calif San Diego, Dept Biomed Informat, La Jolla, CA 92093 USA

[2] UTHlth, Sch Biomed Informat, Houston, TX USA

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS (ICHI) | 2018年

关键词：

Temporal Pattern; Data Mining; Similarity Measure; EHR Data;

D O I：

10.1109/ICHI.2018.00087

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The extraction of temporal patterns from biomedical data is central in facilitating clinical decision support and enabling proactive interventions. Despite several approaches have been proposed for efficiently mining temporal patterns, most of the current solutions offer limited flexibility in addressing the noise in the data. To this end, we propose a similarity measure for temporal patterns in time interval sequences, we develop two mining approaches and conduct a preliminary evaluation on a real world dataset.

引用

页码：434 / 435

页数：2

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