In this paper, we present a manifold model for medical relation extraction. Our model is built upon a medical corpus containing 80M sentences (11 gigabyte text) and designed to accurately and efficiently detect the key medical relations that can facilitate clinical decision making. Our approach integrates domain specific parsing and typing systems, and can utilize labeled as well as unlabeled examples. To provide users with more flexibility, we also take label weight into consideration. Effectiveness of our model is demonstrated both theoretically with a proof to show that the solution is a closed-form solution and experimentally with positive results in experiments. © 2014 Association for Computational Linguistics.