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C2 Cross-Institutional Evaluation of SuperAlarm Algorithm for Predicting In-Hospital...

Randall Lee
Professor of Medicine at University of California, San Francisco
+ 4 speakers
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Machine Learning for Healthcare
August 8, 2020, Online, Los Angeles, CA, USA
Machine Learning for Healthcare
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About speakers

Randall Lee
Professor of Medicine at University of California, San Francisco
Ran Xiao
Assistant Professor at Duke University
Duc Do
Clinical Instructor at UCLA Cardiac Arrhythmia Center
Cheng Ding
MS at Duke University
Xiao Hu
Ann Henshaw Gardiner Distinguished Prof of Nursing, Prof of ECE, BME, Biostats, Neurology, Surgery at Duke University

Dr. Randall Lee is a cardiologist and electrophysiologist whose research focuses on cardiac arrhythmias and vascular regeneration. Lee is interested in improving outcomes for patients who have catheter ablation to treat complex arrhythmias, such as atrial fibrillation (A-fib) and ventricular tachycardia. Specifically, he is active in devising tools and techniques to better treat arrhythmias and prevent strokes. Working with his cardiovascular surgery colleagues, he has developed hybrid procedures combining epicardial and endocardial ablation that can work for patients with persistent A-fib when previous catheter ablation has failed. He has pioneered devices for stroke prevention, including the LARIAT device, and initiated UCSF programs providing device-based therapies for lowering stroke risk. In addition to these endeavors, he has a cardiac tissue engineering laboratory focused on heart muscle repair and reconstruction to treat heart failure and arrhythmias. Lee earned his medical degree from the David Geffen School of Medicine at UCLA, where he also obtained a doctorate in pharmacology. During a second fellowship at UCSF, he subspecialized in cardiac electrophysiology, the study of heart rhythm disorders.

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My research passion lies in utilizing advanced signal processing and machine learning to advance the field of biomedical research. My research interests include understanding neuromotor related diseases (such as Cerebral Palsy and Parkinson's disease) through analyzing invasive and noninvasive eletrophysiologic signals (EEG, LFP, ECoG); and studying the dynamics and early prediction of acute coronary syndrome from pre- and in-hospital ECG recordings. I'm also interested in biomedical informatics using data mining and machine learning techniques to analyze multi-modality data (bedside monitor alarms, electronic health record, vital signs, physiologic data) towards precision patient monitoring in critical care settings.

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Dr. Duc Do is a cardiac electrophysiologist who practices in the downtown LA office and the UCLA Cardiac Arrhythmia Center in Westwood. He sees patient with arrhythmias, and implants all types of devices, including pacemakers, leadless pacemakers, implantable cardioverter defibrillators, and biventricular pacemakers/defibrillators. He also perform complex ablations including for ventricular tachycardia, premature ventricular complexes, atrial fibrillation, and atrial flutter. He is also a clinical instructor at the David Geffen School of Medicine at UCLA, and is board certified in internal medicine and cardiology. Dr. Do received his medical degree from UCLA and then completed his internal medicine residency at Stanford University. He then returned to UCLA, where he completed his cardiovascular medicine and cardiac electrophysiology fellowships. At UCLA, he was also part of the prestigious STAR Program, which trains physician-scientists and stands for Specialty Training and Advanced Research. He is a member of the American Heart Association, the American Cardiology of Cardiology and other professional organizations. Dr. Do was raised in Southern California. He is fluent in Vietnamese, and when he’s not working, enjoys landscape photography and hiking.

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Dr. Xiao Hu, PhD, joined Duke University School of Nursing January 2020 from the University of California San Francisco (UCSF) where he was a professor of Physiological Nursing and Neurological Surgery, a faculty member of Bakar Computational Health Sciences Institute, and a core member of the joint UC Berkeley - UCSF bioengineering graduate program. There he led a highly active research lab consisting of data scientists, postdoctoral fellows, graduate students, and software engineers and closely collaborated with stakeholders in nursing, critical care/neurocritical care medicine, neurosurgery, hospital medicine, cardiology, FDA, NASA, and industry to develop intelligent systems driven by biomedical and clinical knowledge, rich data from electronic health record and medical devices. Dr. Hu has been the principal investigator of four NIH R01, three NIH R21 research projects, and several industrial grants and has published more than 110 journal papers and been awarded six US patents. He is also a standing member of NIH/CSR Biomedical Computing and Health Informatics study section. He has mentored numerous postdoctoral fellows and served as an advisor for a number of PhD students.

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Hi there, my name is Michelle. I'm out of system, Fraser at Duke University today. It's my pleasure to share with you guys. I was tardy name of the Cross. Institutional evaluation I was couple of hours and 4 prediction. Your hospital could prevent this is a collaborative study among University UCI and UCLA. Bedside monitors are, where do they adopted in your child care? Unit amount in hospitals. However, they are known to produce excessive out of a false alarm. No, don't leave the resulting in so-called alarm 40, but I

also called United or even basic clinical interventions. That's my alarm for 2 as well. The top technology address in other hospitals. In recent years has been available in hospitals, such as a lab test results and use data, mining and machine learning to find Republic of Nations that have predictive up a targeted clinical. You band performed really well in the hospital. Are the same time offering significant reduction in force alarm rate, however is generalizability across different institution has not been started yet. So it is kind of started between the soup alarm from one

institution and we'll test The Edge performance on post is to internal and external Institute no data, but he is generally generalizability and the other performers in time show photos of consistency and also some discrepancy between internal and external institutional performance, And our future effort will be directed to a study of what effects are called this discrepancy and how we can improve your generalizability. If you're interested to learn more about the topic, please feel free to

stop by or what we're supposed to do in the conference. Thank you.

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Randall Lee
Ran Xiao
Duc Do
Cheng Ding
Xiao Hu