DICE : Data-Efficient Clinical Event Extraction with Generative Models (2023)

Abstract: Event extraction for the clinical domain is an under-explored research area. The lack of training data along with the high volume of domain-specific terminologies with vague entity boundaries makes the task especially challenging. In this paper, we introduce DICE, a robust and data-efficient generative model for clinical event extraction. DICE frames event extraction as a conditional generation problem and introduces a contrastive learning objective to accurately decide the boundaries of biomedical mentions. DICE also trains an auxiliary mention identification task jointly with event extraction tasks to better identify entity mention boundaries, and further introduces special markers to incorporate identified entity mentions as trigger and argument candidates for their respective tasks. To benchmark clinical event extraction, we compose MACCROBAT-EE, the first clinical event extraction dataset with argument annotation, based on an existing clinical information extraction dataset MACCROBAT. Our experiments demonstrate state-of-the-art performances of DICE for clinical and news domain event extraction, especially under low data settings.

## introduction event extraction (ee) is an information extraction task that aims to identify event triggers and arguments from unstructured texts @xcite . the ee task consists of two subtasks: 1) event detection, in which the model extracts trigger text and predicts the event type; and 2) event argument extraction, in which the model extracts argument text and predicts the role of each argument given an event trigger and associated event type. clinical ee aims to extract clinical events, which are occurrences at specific points in time during a clinical process, such as diagnostic procedures, symptoms, etc. the arguments for such events are ## the dice event extraction model we formulate ee as a conditional generation task, so that we can incorporate domain knowledge such as event type and argument role definitions via natural language in the input prompt. to tackle the challenges of clinical ee, we 1) further enhance the ee model's specialization in mention identification by techniques introduced in §4.2 to handle long clinical mentions with vague boundaries; and 2) perform an independent query for each event type/argument role for better long-tail performance in settings with many event types/argument roles as introduced in §4.1. figure 2 shows the model design. ## experiments in the clinical domain we evaluate dice on maccrobat-ee and compare it with existing event extraction models. ## experiments in the general domain we evaluate dice's generalizability by performing ee on the widely-used news-domain dataset ace05 @xcite , which contains 33 event types and 22 distinct argument roles. we perform both full-shot and low-resource experiments with 10% of the training data using the same data pre-processing, data splits and metrics as prior works @xcite , and we compare with the same set of baselines introduced in §5.1. baseline selection criteria and more results are presented in appendix c.1. we show the result in ## conclusion and future work we present dice, a generative event extraction model designed for the clinical domain. dice is adapted to tackle long and complicated mentions by conducting contrastive learning on instances with mention boundary perturbation, jointly optimizing ee tasks with the auxiliary mention identification task as well as the addition of mention boundary markers. we also introduce maccrobat-ee, the first clinical ee dataset with argument annotation as a testbench for future clinical ee works. lastly, our evaluation shows that dice achieves state-ofthe-art ee performance in the clinical and news domains. in the future, we aim to apply transfer learning from higher-resource domains.