Objective This article summarizes the preparation, organization, evaluation, and results of Track 2 of the 2018 National NLP Clinical Challenges shared task. Track 2 focused on extraction of adverse drug events (ADEs) from clinical records and evaluated 3 tasks: concept extraction, relation classification, and end-to-end systems. We perform an analysis of the results to identify the state of the art in these tasks, learn from it, and build on it. Materials and Methods For all tasks, teams were given raw text of narrative discharge summaries, and in all the tasks, participants proposed deep learning–based methods with hand-designed features. In the concept extraction task, participants used sequence labelling models (bidirectional long short-term memory being the most popular), whereas in the relation classification task, they also experimented with instance-based classifiers (namely support vector machines and rules). Ensemble methods were also popular. Results A total of 28 teams participated in task 1, with 21 teams in tasks 2 and 3. The best performing systems set a high performance bar with F1 scores of 0.9418 for concept extraction, 0.9630 for relation classification, and 0.8905 for end-to-end. However, the results were much lower for concepts and relations of Reasons and ADEs. These were often missed because local context is insufficient to identify them. Conclusions This challenge shows that clinical concept extraction and relation classification systems have a high performance for many concept types, but significant improvement is still required for ADEs and Reasons. Incorporating the larger context or outside knowledge will likely improve the performance of future systems.

Coronary Artery Disease (CAD) is not only the most common form of heart disease, but also the leading cause of death in both men and women[1]. We present a system that is able to automatically predict whether patients develop coronary artery disease based on their narrative medical histories, i.e., clinical free text. Although the free text in medical records has been used in several studies for identifying risk factors of coronary artery disease, to the best of our knowledge our work marks the first attempt at automatically predicting development of CAD. We tackle this task on a small corpus of diabetic patients. The size of this corpus makes it important to limit the number of features in order to avoid overfitting. We propose an ontology-guided approach to feature extraction, and compare it with two classic feature selection techniques. Our system achieves state-of-the-art performance of 77.4% F1 score.