Gulden C, Landerer I, Nassirian A, Altun FB, Andrae J (2019)
Publication Type: Conference contribution
Publication year: 2019
Publisher: IOS Press
City/Town: Amsterdam
Book Volume: 258
Pages Range: 226-230
Conference Proceedings Title: Proceedings of the EFMI 2019 Special Topic Conference
DOI: 10.3233/978-1-61499-959-1-226
Understanding the prevalence of structured data elements within clinical trial eligibility criteria is a crucial step for prioritizing integration efforts to supported automated patient recruitment into clinical trials based on electronic health record data. In this work, we extract data elements from 50 clinical trials using a collaborative, crowd-sourced, and iterative method. A total of 1.120 criteria were analyzed, and 204 unique data elements were extracted. The most prevalent elements were diagnosis code, procedure code, and medication code, occurring in 414 (37 %), 112 (10 %), and 91 (8 %) of eligibility criteria respectively. The results of this study may aid in optimizing data integration and documentation efforts in the EHR to support clinical trial eligibility determination.
APA:
Gulden, C., Landerer, I., Nassirian, A., Altun, F.B., & Andrae, J. (2019). Extraction and prevalence of structured data elements in free-text clinical trial eligibility criteria. In Amnon Shabo, Inge Madsen, Thomas M. Deserno, Matthias Lobe, Kristiina Hayrinen, Hans-Ulrich Prokosch, Fernando Martin-Sanchez, Klaus-Hendrik Wolf (Eds.), Proceedings of the EFMI 2019 Special Topic Conference (pp. 226-230). Amsterdam: IOS Press.
MLA:
Gulden, Christian, et al. "Extraction and prevalence of structured data elements in free-text clinical trial eligibility criteria." Proceedings of the ICT for Health Science Research Ed. Amnon Shabo, Inge Madsen, Thomas M. Deserno, Matthias Lobe, Kristiina Hayrinen, Hans-Ulrich Prokosch, Fernando Martin-Sanchez, Klaus-Hendrik Wolf, Amsterdam: IOS Press, 2019. 226-230.
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