Data Mining: Using Machine Learning for Predictive Neurocritical Care

Over the years working in the neurocritical ICU, Dr. Soojin Park recognized a problem: She knew that 30 to 40% of her patients were at risk for stroke in the weeks following an aneurysmal subarachnoid hemorrhage, but it was still difficult to determine which patients were most likely to develop additional problems, like a delayed cerebral ischemia, and treat them accordingly. So, Dr. Park used her background in data science to develop a tool that can better predict which specific patients were at increased risk. The COSMIC score utilizes machine learning, and basic patient data such as blood pressure and heart rate, to predict likely outcomes, and improve targeted patient care in the neurocritical ICU.