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Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits — PAN-EM-NEURO  

Artificial Intelligence Clinical Trial

Official title:
Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits

Clinical Trial Summary

Future predictive modeling in emergency medicine will likely combine the use of a wide range of data points such as continuous documentation, monitoring using wearables, imaging, biomarkers, and real-time administrative data from all health care providers involved. Subsequent extensive data sets could feed advanced deep learning and neural network algorithms to accurately predict the risk of specific health conditions. Moreover, predictive analytics steers towards the development of clinical pathways that are adaptive and continuously updated, and in which healthcare decision-making is supported by sophisticated algorithms to provide the best course of action effectively and safely. The potential for predictive analytics to revolutionize many aspects of healthcare seems clear in the horizon. Information on the use in emergency medicine is scarce. Aim of the study is to evaluate the performance of using routine-data to predict resource usage in emergency medicine using the commonly encountered symptom of acute neurologic deficit. As an outlook, this might serve as a prototype for other, similar projects using routine medical data for predictive analytics in emergency medicine.

Clinical Trial Description

n/a

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT06245694
Study type Observational
Source Medical University of Vienna
Contact Jan Niederdöckl, MD
Phone 0042 40 400 19640
Email jan.niederdoeckl@meduniwien.ac.at
Status Recruiting
Phase
Start date January 1, 2022
Completion date January 1, 2030
View Details
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