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.
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