Sentence examples for medical relations from inspiring English sources

In 2010, i2b2 together with VA Salt Lake City Health Care System organized a community-wide shared task (19) that had as one of its subtasks to automatically classify TREATMENT events with PROBLEM events, TEST events with PROBLEM events and event pairs each of type PROBLEM into one of a set of predefined semantic medical relations.

This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix.

Rule1 says that if TREATMENT event1 and PROBLEM event2 have a semantic medical relation TrAP, there is a prepositional-for dependency from event1 to event2, and where both events are in numbered argument 2 related to predicate started.

Given the potential usefulness of medical semantic relations for temporal relation classification, as demonstrated earlier through Examples (8)–(12), we create features for temporal relation classification based on these semantic relations.

On the other hand, we hypothesize that medical semantic relations could sometimes provide useful information for temporal relation classification for cases that cannot be handled by domain-independent relations.

Since there does not exist publicly available database that provides medical semantic relations, we develop a system for classifying such relations with the ultimate goal of employing them for temporal relation classification.

Given that a corpus annotated with medical semantic relations exists, we adopt a corpus-based approach to building a medical semantic relation classification system.

On the other hand, medical semantic relations take into account the two events as well as the governing verb 'at the same time', thus providing us with information that cannot be directly inferred from predicate argument relations.

One feature encodes the relation type predicted by our ensemble-based medical semantic relation classification system.

Given they are not related to each other (via any medical semantic relation), it is unlikely that they occur simultaneously.

First, consider Sentence (9), which shows that the absence of a medical semantic relation between two events could also be useful for temporal relation classification.