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.
|Original language||English (US)|
|Title of host publication||MEDINFO 2019|
|Subtitle of host publication||Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics|
|Editors||Brigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi|
|Number of pages||5|
|State||Published - Aug 21 2019|
|Event||17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France|
Duration: Aug 25 2019 → Aug 30 2019
|Name||Studies in Health Technology and Informatics|
|Conference||17th World Congress on Medical and Health Informatics, MEDINFO 2019|
|Period||8/25/19 → 8/30/19|
Bibliographical noteFunding Information:
This work was supported by NIH award LM011972, NSF awards IIS 1602394 and IIS 1602198. The views expressed in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.
© 2019 International Medical Informatics Association (IMIA) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
- Data Mining
- Data Science
- Electronic Health Records