Abstract
BackgroundEHR (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.ResultSN 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’.ConclusionThe results indicate that the methodology followed may be of good value to the diagnostic procedure, especially for analyzing temporal form of clinical data.
Highlights
EHR (Electronic Health Record) system has led to development of specialized form of clinical databases which enable storage of information in temporal prospective
To predict the state of a disease at point Tn, we propose a new algorithm based on Jacobian transformation by considering different temporal points, in which Jacobian of selected clinical parameters are associated with the state of that disease
The set of rules deciphered from association mining with 85% confidence and atleast 50% support criteria suggests that Creatinine ‘c’, Blood Urea Nitrogen (BUN) ‘b’, Serum glutamic oxaloacetic transaminase (SGOT) (Serum Glutamic Oxaloacetic Transaminase) ‘s’ and Serum pyruvic transaminase (SGPT) (Serum Pyruvic Transaminase) ‘g’ are the clinical diagnostic parameters which can be associated with occurrence of brain tumor in patients [26]
Summary
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. We have proposed a new algorithm, SN algorithm, to map clinical parameters along with a disease state at various temporal points. Since there is lack of integration of these data, the importance and relationships among the clinical parameters pertaining to occurrence of diseases is difficult to analyze. Development of novel informatics techniques based on mathematical or statistical models are essential.
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