Clinical and Applied Machine Learning Group (CAML)
We develop machine learning methods inspired by real clinical applications
Our group develops machine learning techniques with clinical inspiration and real-world relevance. Our current research touches on medical imaging, algorithmic fairness, optimization, and causal inference.
We aim to study the causes and transmission modes of infectious diseases among members of a community in the presence of hidden, asymptomatic spreaders of the pathogen.
Transitioning machine learning models across electronic health record (EHR) versions can be improved by mapping different EHR encodings to a common vocabulary.
We aim to utilize supervised machine learning techniques on player tracking data from the game of basketball to automatically discover relationships between player movement and offensive success.
“ScribblePrompt” is an interactive AI framework that can efficiently highlight anatomical structures across different medical scans, assisting medical workers to delineate regions of interest and abnormalities.
By providing plausible label maps for one medical image, the Tyche machine-learning model could help clinicians and researchers capture crucial information.
The MICCAI Society honored Adrian Dalca SM ‘12 PhD ‘16 with the Young Scientist Publication Impact Award for his 2018 work, where his team applied convolutional neural networks to 3D brain scans.
Developed at MIT’s Computer Science and Artificial Intelligence Laboratory, new system uses a patient's ECG signal to estimate potential for cardiovascular death
