At HIMSS20 next month, two machine learning experts will show how machine learning algorithms are evolving to handle complex physiological data and drive more detailed clinical insights. During surgery and other critical care procedures, continuous monitoring of blood pressure to detect and avoid the onset of arterial hypotension is crucial. New machine learning technology developed by Edwards Lifesciences has proven to be an effective means of doing this. In the prodromal stage of hemodynamic instability, which is characterized by subtle, complex changes in different physiologic variables unique dynamic arterial waveform "signatures" are formed, which require machine learning and complex feature extraction techniques to be utilized. Feras Hatib, director of research and development for algorithms and signal processing at Edwards Lifesciences, explained his team developed a technology that could predict, in real-time and continuously, upcoming hypotension in acute-care patients, using an arterial pressure waveforms.