Abstract
Electronic Health Records (EHRs) are typically stored as time-stamped encounter records. Judicious interpretation of temporal relationship between medical records is an integral part of assessing clinical information. Analogously, data analyzed by statistical or data mining methods need to contain time-interdependent analysis variables (TIAVs), whose values represent the clinical embodiment to be investigated. Unlike directly measured data, TIAV formulation is an iterative collaboration between programmer and investigator. This is because clinical TIAVs are context specific and often not absolute, and a custom program is needed to create and assess TIAVs. With rapidly growing interest in mining EHRs, there is a need for scalable solutions to optimize TIAV generation. We describe a framework of using sequences of time-referenced entities as building blocks. Scripts of simple functions are used with these entities to create TIAVs that incorporates multiple interdependencies, hence reducing the need for custom programs. We provide three examples to illustrate the principles of this method using the Veterans Health Administration’s EHR data.
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Woods, A., Meyer, C., Sauer, B., Cohen, B. (2021). Mining Time-Stamped Electronic Health Records with Referenced Sequences. In: Arai, K. (eds) Advances in Information and Communication. FICC 2021. Advances in Intelligent Systems and Computing, vol 1364. Springer, Cham. https://doi.org/10.1007/978-3-030-73103-8_7
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DOI: https://doi.org/10.1007/978-3-030-73103-8_7
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