Model improves prediction of mortality risk in ICU patients
In intensive care units, where patients come in with a wide range of health conditions, triaging relies heavily on clinical judgment. ICU staff run numerous physiological tests, such as bloodwork and checking vital signs, to determine if patients are at immediate risk of dying if not treated aggressively.
Enter: machine learning. Numerous models have been developed in recent years to help predict patient mortality in the ICU, based on various health factors during their stay. These …
MIT News – Electrical engineering and computer science (EECS) – Computer science and technology