End-to-end extraction of temporal information from psychiatric discharge summaries

Among the many types of information embedded in electronic health records that may yield insights into patients' health trajectories, events and the relations between them hold particular promise. But this area is poorly explored, particularly within the psychiatric domain. To fill this gap, we trained five new models to extract events, time ex- pressions, and relations from psychiatric notes. We evaluated these models alongside a similar model trained to do the same task on colon cancer notes. We found that each of the psychiatrically fine-tuned models outperformed the pre-existing model; among the new models, some training choices gave small performance improvements over others.