1. Using machine learning to improve patient care

    Phone: 617-324-9135MIT Computer Science & Artificial Intelligence Lab 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.

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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD

    1. The system could potentially be an aid for doctors in the ICU, which is a high-stress, high-demand environment.
    2. Much of the previous work in clinical decision-making has focused on outcomes such as mortality (likelihood of death), while this work predicts actionable treatments.
    3. Deep neural-network-based predictive models in medicine are often criticized for their black-box nature.
    4. Machine-learning models in health care often suffer from low external validity, and poor portability across sites.
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