Health Analytics Collective uses real-world evidence to modernize health and drug development decisions.

Model identifies instances when autonomous systems have learned from examples that may cause dangerous errors in the real world.

Neural network assimilates multiple types of health data to help doctors make decisions with incomplete information.

MIT researchers have developed a model that can assimilate multiple types of a patient’s health data to help doctors make decisions with incomplete information.

Current approaches to construct road network maps from GPS trajectories suffer from low precision, especially in dense urban areas and in regions with complex topologies such as overpasses and underpasses, parallel roads, and stacked roads. This paper proposes a two-stage method to improve precision without sacrificing recall (coverage).

We present a novel computational framework for high-resolution topology optimization that delivers leaps in simulation capabilities, by two orders of magnitude, from the state-of-the-art approaches.