Discovery Algorithm of Frequent Pattern in Clinical Diabetes Diagnosis

doi:10.3969/j.issn.1006-2475.2016.04.002

Abstract

Abstract:

With the development of information construction process of the major hospitals, various production systems in the hospitals such as HIS (Hospital Information System), EMR (Electronic Medical Record System) have accumulated a largescale clinical data. These clinical big data have a farreaching significance for improving the quality of clinical care. Diabetes as a chronic disease, is apt to cause a variety of complications, such as kidney disease, eye disease and so on. In order to find out the rules of diabetes complications, this article firstly changes the historical data of diabetes diagnosis to event sequence, then proposes a frequent pattern discovery algorithm NFPS for the event sequence of clinical diabetes diagnosis based on the traditional algorithm SPADE. The proposed algorithm takes into account diabetes treatment interval, supports the frequent pattern discovery of the diabetes complications within the set time window. Experimental results show its effectiveness in the frequent pattern discovery of clinical diabetes complications.

Key words: diabetes, complications, event sequence, time window, frequent pattern mining

CLC Number:

TP302

CHEN Lian, LE Jia-jin, CHEN De-hua, FENG Jie-ying.

Discovery Algorithm of Frequent Pattern in Clinical Diabetes Diagnosis[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2016.04.002.

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