Temporal relations are crucial in constructing a timeline over the course of clinical care,
which can help medical practitioners and researchers track the progression of diseases …

Unstructured data in electronic health records, represented by clinical texts, are a vast
source of healthcare information because they describe a patient's journey, including clinical …

In this work, we studied methods to identify direct (intra-sentence) temporal relationships
between clinical events and temporal expressions. In particular, we compared several …

Recently BERT has achieved a state-of-the-art performance in temporal relation extraction
from clinical Electronic Medical Records text. However, the current approach is inefficient as …

Time is an important concept in human-cognition, fundamental to a wide range of reasoning
tasks in the clinical domain. Results of the Clinical TempEval 2016 challenge, a set of …

Objectives This study focuses on refining temporal relation extraction within medical
documents by introducing an innovative bimodal architecture. The overarching goal is to …

Temporal relation extraction between health-related events is a widely studied task in
clinical Natural Language Processing (NLP). The current state-of-the-art methods mostly rely …

Reliable extraction of temporal relations from clinical notes is a growing need in many
clinical research domains. Our work introduces typed markers to the task of clinical temporal …

Abstract Electronic Health Records (EHRs) are generated in the clinical treatment process
and contain a large number of medical knowledge, which is closely related to the health …

Neural network models are oftentimes restricted by limited labeled instances and resort to
advanced architectures and features for cutting edge performance. We propose to build a …