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Title: Evaluating the Impact of Data Representation on EHR-Based Analytic Tasks
Different analytic techniques operate optimally with different types of data. As the use of EHR-based analytics expands to newer tasks, data will have to be transformed into different representations, so the tasks can be optimally solved. We classified representations into broad categories based on their characteristics and proposed a new knowledge-driven representation for clinical data mining as well as trajectory mining, called Severity Encoding Variables (SEVs). Additionally, we studied which characteristics make representations most suitable for particular clinical analytics tasks including trajectory mining. Our evaluation shows that, for regression, most data representations performed similarly, with SEV achieving a slight (albeit statistically significant) advantage. For patients at high risk of diabetes, it outperformed the competing representation by (relative) 20%. For association mining, SEV achieved the highest performance. Its ability to constrain the search space of patterns through clinical knowledge was key to its success.
Authors:
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Award ID(s):
1602394
Publication Date:
NSF-PAR ID:
10168833
Journal Name:
Medinfo 2019
Sponsoring Org:
National Science Foundation
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