SN algorithm: analysis of temporal clinical data for mining periodic patterns and impending augury

Dipankar Sengupta, Pradeep K Naik

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

BACKGROUND: EHR (Electronic Health Record) system has led to development of specialized form of clinical databases which enable storage of information in temporal prospective. It has been a big challenge for mining this form of clinical data considering varied temporal points. This study proposes a conjoined solution to analyze the clinical parameters akin to a disease. We have used "association rule mining algorithm" to discover association rules among clinical parameters that can be augmented with the disease. Furthermore, we have proposed a new algorithm, SN algorithm, to map clinical parameters along with a disease state at various temporal points.

RESULT: SN algorithm is based on Jacobian approach, which augurs the state of a disease 'Sn' at a given temporal point 'Tn' by mapping the derivatives with the temporal point 'T0', whose state of disease 'S0' is known. The predictive ability of the proposed algorithm is evaluated in a temporal clinical data set of brain tumor patients. We have obtained a very high prediction accuracy of ~97% for a brain tumor state 'Sn' for any temporal point 'Tn'.

CONCLUSION: The results indicate that the methodology followed may be of good value to the diagnostic procedure, especially for analyzing temporal form of clinical data.

Original languageEnglish
Pages (from-to)24
JournalJournal of clinical bioinformatics
Volume3
Issue number1
DOIs
Publication statusPublished - 28 Nov 2013
Externally publishedYes

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