MIT researchers use machine learning to predict ICU interventions

Researchers at the Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory have developed a machine learning algorithm that leverages large amounts of intensive care unit (ICU) data to predict actionable interventions for patients and improve health outcomes. By tapping into an MIT database of de-identified data for 40,000 critical care patients--including demographics, laboratory tests, medications and vital signs--the research team is able to use deep learning to determine what kinds of treatments are needed for different symptoms. The approach--called ICU Intervene--was presented in a paper this past weekend at the Machine Learning for Healthcare Conference in Boston. According to the authors, their model is the first to use deep neural networks to predict both onset and weaning of interventions using all available modalities of ICU data. "The decisions that are made in the ICU are made in a particularly high-stress and high-demand environment," says Harini Suresh, a PhD student and lead author on the paper, who adds that clinicians in these situations are bombarded with different types of data for many patients and as a result it can be difficult to make real-time treatment decisions.