TY  - CONF
AU  - Fonck, Simon
AU  - Fritsch, Sebastian
AU  - Pieper, Hannes
AU  - Baron, Alexander
AU  - Kowalewski, Stefan
AU  - Stollenwerk, André
TI  - Retrospective Classification of ARDS in ICU Time-series data using Random Forest with a focus on Data Pre-processing
VL  - 58
IS  - 24
SN  - 1474-6670
CY  - Laxenburg
PB  - IFAC
M1  - FZJ-2024-06558
T2  - IFAC-PapersOnLine
SP  - 129 - 134
PY  - 2024
AB  - 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.
T2  - 17th Interdisciplinary Symposium Automed
CY  - 11 Sep 2024 - 13 Sep 2024, Villingen-Schwenningen (Germany)
Y2  - 11 Sep 2024 - 13 Sep 2024
M2  - Villingen-Schwenningen, Germany
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
UR  - <Go to ISI:>//WOS:001359709100023
DO  - DOI:10.1016/j.ifacol.2024.11.024
UR  - https://juser.fz-juelich.de/record/1033700
ER  -