Abstract
Personalized medicine exploits the patient data, for example, genetic compositions, and key biomarkers. During the data mining process, the key challenges are the information loss, the data types heterogeneity and the time series representation. In this paper, a novel data representation model for personalized medicine is proposed in light of these challenges. The proposed model will account for the structured, temporal and non-temporal data and their types, namely, numeric, nominal, date, and Boolean. After the "Date and Boolean" data transformation, the nominal data are treated by dispersion while several clustering techniques are deployed to control the numeric data distribution. Ultimately, the transformation process results in three homogeneous representations with these representations having only two dimensions to ease the exploration of the represented dataset. Compared to the Symbolic Aggregate Approximation technique, the proposed model preserves the time-series information, conserves as much data as possible and offers multiple simple representations to be explored.
Highlights
Personalized medicine (PM) refers to the individualization of medical treatments based on the unique dataset of each patient
The process of reporting, evaluation, and medical decision-making based on the Electronic Health Record (EHR) data involves the extraction of relevant information and knowledge via specialized methods known as data mining techniques
In line with the minimum length constraint vis-à-vis the list of different values applied on the numeric events to be partitioned, we have found that a single case generated by the event "980" has three (3) different values so that the clustering process is executed only for k= 2 and k= 3
Summary
Personalized medicine (PM) refers to the individualization of medical treatments based on the unique dataset of each patient. An EHR refers to a longstanding, comprehensive health database resource that stores and manages all patient data files digitally under the custody of a licensed health entity It provides a digitalized view of the patient’s demographics, data associated with the patient’s clinical and medication history, diagnostic trajectory, social and economic environmental conditioning, geographical relocation, if any, as well as the patient’s genetic data, if these exist (Jensen et al, 2012). The objective of our work is to produce a high fidelity model for the representation of PM structured data This is a challenging problem and our proposed model addresses several important scientific gaps: data heterogeneity, loss of data during data transformation, and interpretability of the representation over the course of a data mining process. This paper emphasizes the need to explore the EHR data mining process that informs and challenges PM, which will enhance the ability of physicians and other care professionals to personalize high quality care to the inflicted individuals
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