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Data Mining: Using Machine Learning for Predictive Neurocritical Care

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Manage episode 466011306 series 3645759
Content provided by NewYork-Presbyterian. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by NewYork-Presbyterian or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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.

  continue reading

34 episodes

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iconShare
 
Manage episode 466011306 series 3645759
Content provided by NewYork-Presbyterian. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by NewYork-Presbyterian or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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.

  continue reading

34 episodes

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