Made by DATEXIS (Data Science and Text-based Information Systems) at Beuth University of Applied Sciences Berlin
Deep Learning Technology: Sebastian Arnold, Betty van Aken, Paul Grundmann, Felix A. Gers and Alexander Löser. Learning Contextualized Document Representations for Healthcare Answer Retrieval. The Web Conference 2020 (WWW'20)
Funded by The Federal Ministry for Economic Affairs and Energy; Grant: 01MD19013D, Smart-MD Project, Digital Technologies
Current research seeks to predict the event of rearrest after patients have already achieved ROSC. Biosignals, such as electrocardiogram (ECG), have the potential to predict the onset of rearrest and are currently being investigated to preemptively warn health care providers that rearrest could be imminent.
A stronger pulse detector would also contribute to lowering the rate of rearrest. If the resuscitator could accurately know when the patient has achieved ROSC, there would be less instances of chest compressions being provided when a native pulse is present.
A recent study by Salcido et al. (2010) ascertained rearrest in all initial and rearrest rhythms treated by any level of Emergency Medical Service (EMS), finding a rearrest rate of 36% and a lower but not significantly different rate of survival to hospital discharge in cases with rearrest compared to those without rearrest.