MIT projects explore machine learning applications to improve EHRs

Two new studies from MIT's Computer Science and Artificial Intelligence Laboratory shed light on ways machine learning can improve electronic health records and predictive analytics to help physicians make more informed decisions. As doctors grapple with a profusion data across multiple systems, with charts documented in varying degrees of consistency, the challenges of putting it all to use for real-time decision-making is acute. Teams at CSAIL have tackled a pair of projects they say could help make EHRs work better for hospital clinicians. Both models were made possible by MIMIC, an open dataset developed by the MIT Lab for Computational Physiology that has deidentified health data for 40,000 critical care patients. One project uses machine-learning for an approach called "ICU Intervene," which processes troves of data from the intensive-care-unit and applies deep learning processes to sift through lab results, vitals demographic information and more to help physicians make real-time predictions.