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Data Mining: Using Machine Learning for Predictive Neurocritical Care
FromAdvances in Care
ratings:
Length:
21 minutes
Released:
May 16, 2024
Format:
Podcast episode
Description
Monitoring patients with aneurysmal rupture for delayed cerebral ischemia was historically a numbers game. It was difficult for doctors to predict outcomes in the weeks that followed their rupture, so at-risk patients could find themselves under observation in the ICU anywhere from 7 to 21 days. Dr. Soojin Park, Medical Director of Critical Care Data Science and AI at NewYork-Presbyterian/Columbia, knew there had to be a better way to monitor patients and predict outcomes. So, relying on her background in machine learning and leveraging vast amounts of data, Dr. Park developed the potentially game-changing Continuous Monitoring Tool for Delayed Cerebral Ischemia (or COSMIC) score. The score uses machine learning, and basic patient data that can be collected with equipment available at any hospital, to detect signals that more accurately assess risk, allowing doctors to treat each neurocritical patient with targeted care - ultimately improving outcomes and patient experience.
Released:
May 16, 2024
Format:
Podcast episode
Titles in the series (26)
Welcome to Advances in Care: Join science journalist Catherine Price as she sits down with top physicians from NewYork-Presbyterian Hospital to discuss the latest research, complex cases, and what it means to deliver patient-centered care. by Advances in Care