Work

Retrospective Classification of ARDS in ICU Time-series data using Random Forest with a focus on Data Pre-processing

Year: 2024

Type: article

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.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2024.11.024

Authors: Simon Fonck, Sebastian Fritsch, Hannes Pieper, Alexander Baron, Stefan Kowalewski, André Stollenwerk

Institutions: Forschungszentrum Jülich, RWTH Aachen University, Universitätsklinikum Aachen, Jülich Supercomputing Centre

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4404524391

FWCI: 0.149

Cites: 22

Cited by: 1

Related to: 10