Actively Recruiting
Led by Medical University of Vienna · Updated on 2024-11-22
50000
Participants Needed
1
Research Sites
260 weeks
Total Duration
Researchers are exploring how predictive analytics can improve emergency medicine by using a broad range of data such as continuous documentation, wearable monitoring, imaging, biomarkers, and real-time administrative information from healthcare providers. The study focuses on using routine data to predict resource usage in emergency cases presenting with acute neurological deficits. This approach aims to serve as a prototype for similar projects applying advanced data analytics in emergency settings. This observational study does not involve treatments but collects and analyzes routine medical data to develop and evaluate predictive models. The project will employ advanced deep learning and neural network algorithms to assess how accurately these models can forecast resource needs in emergency medicine. The study is sponsored by the Medical University of Vienna and began in 2022. Participants aged 18 years and older of any gender may take part, with no other exclusion criteria. The study will primarily measure the success of its prediction model by January 1, 2025. Participants' routine health data will be used without intervention, and no additional procedures or treatments are required. The study's results may help improve clinical decision-making and resource allocation in emergency care.
CONDITIONS
Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits
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Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
Duration - Up to 5 years
Participants are observed to collect data related to neurological deficits in emergency medicine.
Total: 1 location
1
Emergency Department, Medical University Vienna
Vienna, Austria, 1090
Actively Recruiting
J
Jan Niederdöckl, MD
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
0
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