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

Journal of Clinical Bioinformatics

Pradeep K Naik¹  Dipankar Sengupta¹ 

[1] Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Solan, H.P, India

关键词: Temporal;    Jacobian determinant;    Jacobian;    Data mining;    Clinical informatics;    Association rule mining;   

Others  :  801292
DOI  :  10.1186/2043-9113-3-24

 received in 2013-09-27, accepted in 2013-11-25,  发布年份 2013

【 摘 要 】

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 ‘S_n’ at a given temporal point ‘T_n’ 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 ‘S_n’ for any temporal point ‘T_n’.

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.

【 授权许可】

   
2013 Sengupta and Naik; licensee BioMed Central Ltd.

【 图 表 】

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