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Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic stroke using artificial neural networks
ConclusionThe artificial neural network model achieved adequate discriminative power for predicting prolonged length of stay after acute ischemic stroke and identified crucial factors associated with a prolonged hospital stay. The proposed model can assist in clinically assessing the risk of prolonged hospitalization, informing decision-making, and developing individualized medical care plans for patients with acute ischemic stroke.
Source: Frontiers in Neurology - February 9, 2023 Category: Neurology Source Type: research
Time is Brain - Preclinical Emergency Care for Acute Ischemic Stroke
Anasthesiol Intensivmed Notfallmed Schmerzther. 2021 Dec;56(11-12):746-759. doi: 10.1055/a-1330-5239. Epub 2021 Nov 24.ABSTRACTStroke is one of the most common neurological emergencies and requires rapid detection and treatment (time is brain). There is still insufficient knowledge about stroke warning signs. It is therefore of crucial importance that trained personnel in the preclinical setting recognize the relevant warning symptoms and collect the necessary information to quickly refer the patient to the appropriate additional care structure. For this purpose, training of the emergency medical services (EMS) and the cor...
Source: Anasthesiologie, Intensivmedizin, Notfallmedizin, Schmerztherapie : AINS - November 25, 2021 Category: Intensive Care Authors: Leona M öller Lars Timmermann Anja Gerstner Source Type: research
