Doctors are often deluged by signals from charts, test results, and other metrics to keep track of. It can be difficult to integrate and monitor all of these data for multiple patients while making real-time treatment decisions, especially when data is documented inconsistently across hospitals. In a new pair of papers, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) explore ways for computers to help doctors make better medical decisions. One team created a machine-learning approach called "ICU Intervene" that takes large amounts of intensive-care-unit (ICU) data, from vitals and labs to notes and demographics, to determine what kinds of treatments are needed for different symptoms. The system uses "deep learning" to make real-time predictions, learning from past ICU cases to make suggestions for critical care, while also explaining the reasoning behind these decisions.

Quite possibly, doctors have a tough job to do monitoring several patients at one time, and sometimes standards can slip when they're given too heavy a workload. However, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have set put to change that by integrating machine learning techniques into patient care and to help doctors make better decisions. One approach created was named "ICU Intervene" and is a machine learning approach that processes large amounts of intensive care unit (ICU) data to figure out what treatments are the best option for the different symptoms presented. Deep learning is used to allow the computers to make real-time predictions by learning from past ICU cases. Lead author on the study and Ph.D. student, Harini Suresh, says "The system could potentially be an aid for doctors in the ICU, which is a high-stress, high-demand environment. The goal is to leverage data from medical records to improve health care and predict actionable interventions."

Doctors are often deluged by signals from charts, test results, and other metrics to keep track of. It can be difficult to integrate and monitor all of these data for multiple patients while making real-time treatment decisions, especially when data is documented inconsistently across hospitals. In a new pair of papers, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) explore ways for computers to help doctors make better medical decisions. One team created a machine-learning approach called "ICU Intervene" that takes large amounts of intensive-care-unit (ICU) data, from vitals and labs to notes and demographics, to determine what kinds of treatments are needed for different symptoms. The system uses "deep learning" to make real-time predictions, learning from past ICU cases to make suggestions for critical care, while also explaining the reasoning behind these decisions.