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

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 ‘T(0)’, whose state of disease ‘S(0)’ 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.