An Extensive Data Processing Pipeline for MIMIC-IV

Abstract

An increasing amount of research is being devoted to applying machine learning methods to electronic health record (EHR) data for various clinical tasks. This growing area of research has exposed the limitation of accessibility of EHR datasets for all, as well as the reproducibility of different modeling frameworks. One reason for these limitations is the lack of standardized pre-processing pipelines. MIMIC is a freely available EHR dataset in a raw format that has been used in numerous studies. The absence of standardized pre-processing steps serves as a major barrier to the wider adoption of the dataset. It also leads to different cohorts being used in downstream tasks, limiting the ability to compare the results among similar studies. Contrasting studies also use various distinct performance metrics, which can greatly reduce the ability to compare model results. In this work, we provide an end-to-end fully customizable pipeline to extract, clean, and pre-process data; and to predict and evaluate the fourth version of the MIMIC dataset (MIMIC-IV) for ICU and non-ICU-related clinical time-series prediction tasks. The tool is publicly available at https://github.com/healthylaife/MIMIC-IV-Data-Pipeline.© .

Forecasting Acute Kidney Injury and Resource Utilization in ICU patients using longitudinal, multimodal models

“…In this study, we utilized the complete MIMIC-IV database 48 , which includes approximately 73,181 ICU stays across 50,920 critical care patients. We curated time series data from this resource inspired by prior work 41,49,50 . Figure 3 summarizes the data extraction and processing steps.…”