Sengupta Dipankar, Naik Pradeep K
Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Solan, H,P, India.
J Clin Bioinforma. 2013 Nov 28;3(1):24. doi: 10.1186/2043-9113-3-24.
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.
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'.
The results indicate that the methodology followed may be of good value to the diagnostic procedure, especially for analyzing temporal form of clinical data.
电子健康记录（EHR）系统推动了特殊形式临床数据库的发展，这种数据库能够按时间顺序存储信息。考虑到不同的时间点，挖掘这种形式的临床数据一直是一项巨大挑战。本研究提出了一种联合解决方案，用于分析类似于某种疾病的临床参数。我们使用“关联规则挖掘算法”来发现临床参数之间的关联规则，这些规则可以与该疾病相关联。此外，我们还提出了一种新算法，即SN算法，用于在不同时间点映射临床参数以及疾病状态。
SN算法基于雅可比方法，通过将导数与已知疾病状态“S0”的时间点“T0”进行映射，来预测给定时间点“Tn”的疾病状态“Sn”。在脑肿瘤患者的时间临床数据集中评估了所提算法的预测能力。对于任何时间点“Tn”的脑肿瘤状态“Sn”，我们都获得了约97%的极高预测准确率。
结果表明，所采用的方法可能对诊断程序具有重要价值，特别是在分析临床数据的时间形式方面。
