A Data Representation Model for Personalized Medicine
نویسندگان
چکیده
Personalized medicine exploits the patient data, for example, genetic compositions, and key biomarkers. During data mining process, challenges are information loss, types heterogeneity time series representation. In this paper, a novel representation model personalized is proposed in light of these challenges. The will account structured, temporal non-temporal their types, namely, numeric, nominal, date, Boolean. After "Date Boolean" transformation, nominal treated by dispersion while several clustering techniques deployed to control numeric distribution. Ultimately, transformation process results three homogeneous representations with having only two dimensions ease exploration represented dataset. Compared Symbolic Aggregate Approximation technique, preserves time-series information, conserves as much possible offers multiple simple be explored.
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ژورنال
عنوان ژورنال: International Journal of Healthcare Information Systems and Informatics
سال: 2022
ISSN: ['1555-3396', '1555-340X']
DOI: https://doi.org/10.4018/ijhisi.295822