Contribution to a conference proceedings/Contribution to a book

FZJ-2024-06558

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Retrospective Classification of ARDS in ICU Time-series data using Random Forest with a focus on Data Pre-processing

Fonck, S. (Corresponding author) ; Fritsch, S.FZJ* ; Pieper, H. ; Baron, A. ; Kowalewski, S. ; Stollenwerk, A.

2024
IFAC Laxenburg

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Please use a persistent id in citations: doi:10.1016/j.ifacol.2024.11.024  doi:10.34734/FZJ-2024-06558

Abstract: Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury associated with high mortality. Epidemiological studies have shown that ARDS is often diagnosed too late or not at all. Artificial intelligence (AI) can help clinicians identify ARDS and initiate appropriate therapy earlier. Various data must be collected and processed for the training of such AI methods. It is particularly important to consider the data basis and describe the pre-processing steps of the data, as this has a major influence on the results of an AI model. A random forest algorithm is proposed to automatically assess a patient’s condition for compatibility with an ARDS using time-series data (like vital signs, laboratory values and other parameters). We emphasize the data preparation and its influence on the results. The model achieved moderate to excellent results depending on the preparation and dataset.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)
2. Center for Advanced Simulation and Analytics (CASA)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2024

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Database coverage:
; ; ; SCOPUS

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