Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities.

Autor:	Maletzky A; Research Department Medical Informatics, RISC Software GmbH, Hagenberg, Austria., Böck C; JKU LIT SAL eSPML Lab, Institute of Signal Processing, Johannes Kepler University, Linz, Austria., Tschoellitsch T; Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University, Linz, Austria., Roland T; ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University, Linz, Austria., Ludwig H; ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University, Linz, Austria., Thumfart S; Research Department Medical Informatics, RISC Software GmbH, Hagenberg, Austria., Giretzlehner M; Research Department Medical Informatics, RISC Software GmbH, Hagenberg, Austria., Hochreiter S; ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University, Linz, Austria., Meier J; Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University, Linz, Austria.
Jazyk:	angličtina
Zdroj:	JMIR medical informatics [JMIR Med Inform] 2022 Oct 21; Vol. 10 (10), pp. e38557. Date of Electronic Publication: 2022 Oct 21.
DOI:	10.2196/38557
Abstrakt:	Electronic health records (EHRs) have been successfully used in data science and machine learning projects. However, most of these data are collected for clinical use rather than for retrospective analysis. This means that researchers typically face many different issues when attempting to access and prepare the data for secondary use. We aimed to investigate how raw EHRs can be accessed and prepared in retrospective data science projects in a disciplined, effective, and efficient way. We report our experience and findings from a large-scale data science project analyzing routinely acquired retrospective data from the Kepler University Hospital in Linz, Austria. The project involved data collection from more than 150,000 patients over a period of 10 years. It included diverse data modalities, such as static demographic data, irregularly acquired laboratory test results, regularly sampled vital signs, and high-frequency physiological waveform signals. Raw medical data can be corrupted in many unexpected ways that demand thorough manual inspection and highly individualized data cleaning solutions. We present a general data preparation workflow, which was shaped in the course of our project and consists of the following 7 steps: obtain a rough overview of the available EHR data, define clinically meaningful labels for supervised learning, extract relevant data from the hospital's data warehouses, match data extracted from different sources, deidentify them, detect errors and inconsistencies therein through a careful exploratory analysis, and implement a suitable data processing pipeline in actual code. Only few of the data preparation issues encountered in our project were addressed by generic medical data preprocessing tools that have been proposed recently. Instead, highly individualized solutions for the specific data used in one's own research seem inevitable. We believe that the proposed workflow can serve as a guidance for practitioners, helping them to identify and address potential problems early and avoid some common pitfalls. (©Alexander Maletzky, Carl Böck, Thomas Tschoellitsch, Theresa Roland, Helga Ludwig, Stefan Thumfart, Michael Giretzlehner, Sepp Hochreiter, Jens Meier. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 21.10.2022.)
Databáze:	MEDLINE
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