ArticleStudies in health technology and informatics2019
Evaluating the Impact of Data Representation on EHR-Based Analytic Tasks.
Article in Studies in health technology and informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19.Artificial intelligence in medicine · 2024Article
- Patient Representation From Structured Electronic Medical Records Based on Embedding Technique: Development and Validation Study.JMIR medical informatics · 2021Article
- A Computational Method for Learning Disease Trajectories From Partially Observable EHR Data.IEEE journal of biomedical and health informatics · 2021Article
- Different Data Mining Approaches Based Medical Text Data.Journal of healthcare engineering · 2021Review
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Authors and funding
7 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.