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.

Machine learning, the most fundamental form of artificial intelligence, has started infiltrating the medical field, and it seems machines can play a crucial role in improving our health. A study of over 50 executives in the healtcare sector by TechEmergence revealed that by 2025 AI will be adopted on a broader scale. If there's one thing the healthcare industry has in abundance, it's undoubtedly data. And machine learning algorithms work better if they are exposed to more data. The savings would also be huge.

If it wasn't for a mysterious hot pepper allergy, Harini Suresh might never have found a way to improve patient care in intensive care units. Suresh, a doctoral student at MIT, wants to use AI to help critical care doctors choose the best treatment for each patient. That's not easy when patients are sick with dire conditions like heart failure or stroke, and doctors must quickly weigh vast and varied patient data that may range from simple demographics to complex lab tests. "The ICU is a high-stakes, high-demand environment, and doctors can spend only a limited amount of time with each patient," said Suresh. "When doctors are dealing with many data sources and data types, computational tools can make a difference."

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."

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.

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.

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.