Researchers at the Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory are investigating how computers can enhance medical decisions.
One team developed ICU Intervene, a machine-learning method that processes large intensive-care-unit (ICU) datasets to ascertain what kinds of treatments are needed for different symptoms. The system applies deep learning to make real-time hourly predictions of five different interventions, gaining knowledge from past ICU cases to make critical care suggestions while also articulating the reasoning behind the decisions.
Meanwhile, another team designed the EHR Model Transfer approach to enable predictive models based on an electronic health record (EHR) system, despite being trained on data from a different EHR system. The researchers say their approach, which uses natural language processing to recognize critical concepts that are encoded differently across systems, demonstrates that predictive models for mortality and prolonged hospitalization can be trained on one EHR system and used to make predictions in another.
From MIT News
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