MIT computer scientists are hoping to accelerate the use of artificial intelligence to improve medical decision-making, by automating a key step that’s usually done by hand — and that’s becoming more laborious as certain datasets grow ever-larger.
MIT computer scientists are hoping to accelerate the use of artificial intelligence to improve medical decision-making, by automating a key step that’s usually done by hand — and that’s becoming more laborious as certain datasets grow ever-larger.
The field of predictive analytics holds increasing promise for helping clinicians diagnose and treat patients. Machine-learning models can be trained to find patterns in patient data to aid in sepsis care, design safer chemotherapy regimens, and predict a patient’s risk of having breast cancer or dying in the ICU, to name just a few examples.
Typically, training datasets consist of many sick and healthy subjects, but with relatively little data for each subject. Experts must then find just those aspects — or “features” — in the datasets that will be important for making predictions.
Read more at Massachusetts Institute of Technology
Image: A new MIT-developed model automates a critical step in using AI for medical decision making, where experts usually identify important features in massive patient datasets by hand. The model was able to automatically identify voicing patterns of people with vocal cord nodules (shown here) and, in turn, use those features to predict which people do and don’t have the disorder. Image courtesy of the researchers
