FIBER: enabling flexible retrieval of electronic health records data for clinical predictive modeling
| Autor: | Suparno Datta, Philipp Bode, Benjamin S. Glicksberg, Tom Martensen, Harry Freitas Da Cruz, Erwin P. Bottinger, Ariane Morassi Sasso, Jan Philipp Sachs |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
AcademicSubjects/SCI01060
databases Computer science Process (engineering) workflow Health Informatics Research and Applications information storage and retrieval 03 medical and health sciences 0302 clinical medicine Software 030212 general & internal medicine software/instrumentation 030304 developmental biology computer.programming_language 0303 health sciences Information retrieval business.industry Python (programming language) Data warehouse Schema (genetic algorithms) Workflow electronic health records Data extraction Star schema factual AcademicSubjects/SCI01530 business AcademicSubjects/MED00010 computer |
| Zdroj: | JAMIA Open |
| ISSN: | 2574-2531 |
| Popis: | Objectives The development of clinical predictive models hinges upon the availability of comprehensive clinical data. Tapping into such resources requires considerable effort from clinicians, data scientists, and engineers. Specifically, these efforts are focused on data extraction and preprocessing steps required prior to modeling, including complex database queries. A handful of software libraries exist that can reduce this complexity by building upon data standards. However, a gap remains concerning electronic health records (EHRs) stored in star schema clinical data warehouses, an approach often adopted in practice. In this article, we introduce the FlexIBle EHR Retrieval (FIBER) tool: a Python library built on top of a star schema (i2b2) clinical data warehouse that enables flexible generation of modeling-ready cohorts as data frames. Materials and Methods FIBER was developed on top of a large-scale star schema EHR database which contains data from 8 million patients and over 120 million encounters. To illustrate FIBER’s capabilities, we present its application by building a heart surgery patient cohort with subsequent prediction of acute kidney injury (AKI) with various machine learning models. Results Using FIBER, we were able to build the heart surgery cohort (n = 12 061), identify the patients that developed AKI (n = 1005), and automatically extract relevant features (n = 774). Finally, we trained machine learning models that achieved area under the curve values of up to 0.77 for this exemplary use case. Conclusion FIBER is an open-source Python library developed for extracting information from star schema clinical data warehouses and reduces time-to-modeling, helping to streamline the clinical modeling process. |
| Databáze: | OpenAIRE |
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