Claudio Daza
ABSTRACT
Adequate reutilization of routinely generated clinical data is a key component of what has been called a learning healthcare system, a system that is able to generate enough data that can be then analyzed to generate new insights into what works and what doesn't. However, the reutilization of electronic clinical data is not trivial since the quality of such data is usually low or unknown. Several tools have been developed to extract structured data from electronic health records (EHRs)--such as natural language processing--but, to this day, most researchers and quality experts rely on manual data extraction from EHRs. Here we assess the accuracy of ClincalTime, a temporal abstraction and query system designed easily identify patient cohorts based on patterns of clinical time intervals.
- D. Capurro, M. Barbe, C. Daza, J. Santa María, J. Trincado, and I. Gomez. Clinicaltime: Identification of patients with acute kidney injury using temporal abstractions and temporal pattern matching. AMIA Summits on Translational Science Proceedings, 2015:46, 2015.Google Scholar
- M. Saeed, M. Villarroel, A. T. Reisner, G. Clifford, L.-W. Lehman, G. Moody, T. Heldt, T. H. Kyaw, B. Moody, and R. G. Mark. Multiparameter intelligent monitoring in intensive care ii (MIMIC-II): a public-access intensive care unit database. Critical Care Medicine, 39(5):952, 2011.Google ScholarCross Ref
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