Introducing a Machine Learning Framework for Decision Support in an Acute Healthcare Setting at the Example of Copenhagen EMS
Tuesday, May 12, 2020
Emergency medical dispatchers, in the most advanced healthcare regions, are unsuccessful in identifying approximately 25% of out of hospital cardiac arrest cases, therefore losing the opportunity to provide sufficient resuscitative efforts. Lars will give insight into a case-study on Copenhagen emergency medical services (EMS), and how Corti has developed a decision support system, based on a machine learning framework, enhancing the decision-making of emergency medical dispatchers, in real-time. Results from the Danish National Institute of Public Health, Copenhagen EMS, and the University of Copenhagen showcase how Corti’s software is accurate in 93% of cases versus 73% for human emergency medical dispatchers while being significantly faster in determining the appropriate diagnosis. He will also embed the audience of the newest research, showing similar results, for two US EMS’s. The key takeaways from the talk will be the journey on implementing real-time machine learning models directly on speech signals and how Corti utilises this information for decision-making. Lars will also give insight into Corti’s research ventures within the scope of unsupervised machine learning.