Abstract
"Predicting Delays in Acute Ischemic Stroke Care with Machine Learning"
Highlights
Neural networks have demonstrated remarkable success in extracting intricate patterns from complex data
Machine learning with neural networks have demonstrated remarkable success in finding patterns in structured, as well as unstructured data
With 15-year historical data from the stroke thrombolysis program at a tertiary care center, an attempt was made to design a neural network with the aim of predicting team performance, in terms of door to needle time
Summary
Neural networks have demonstrated remarkable success in extracting intricate patterns from complex data. With 15-year historical data from the stroke thrombolysis program at a tertiary care center, an attempt was made to design a neural network with the aim of predicting team performance, in terms of door to needle time (defined as the time interval from arrival at the hospital to administration of the thrombolytic drug). Such information could be of help to warn of potential delay when a particular patient arrives, and the team armed with this information will be better enabled to pre-empt the delay [2]
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