FIBER: enabling flexible retrieval of electronic health records data for clinical predictive modeling

Suparno Datta; Jan Philipp Sachs; Harry FreitasDa Cruz; Tom Martensen; Philipp Bode; Ariane Morassi Sasso; Benjamin S Glicksberg; Erwin Böttinger

doi:10.1093/jamiaopen/ooab048

FIBER: enabling flexible retrieval of electronic health records data for clinical predictive modeling

JAMIA Open. 2021 Aug 2;4(3):ooab048. doi: 10.1093/jamiaopen/ooab048. eCollection 2021 Jul.

Authors

Suparno Datta^{1

2}, Jan Philipp Sachs^{1

2}, Harry FreitasDa Cruz^{1

2}, Tom Martensen¹, Philipp Bode¹, Ariane Morassi Sasso^{1

2}, Benjamin S Glicksberg^{2

3}, Erwin Böttinger^{1

2}

Affiliations

¹ Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany.
² Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
³ Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

Abstract

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.

Keywords: databases; electronic health records; factual; information storage and retrieval; software/instrumentation; workflow.