Objective This article methodically reviews the literature on deep learning (DL) for natural
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …

Current models for correlating electronic medical records with-omics data largely ignore
clinical text, which is an important source of phenotype information for patients with cancer …

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

Temporal information extraction is a challenging but important area of automatic natural
language understanding. Existing approaches annotate and extract various parts of the …

Time is deeply woven into how people perceive, and communicate about the world. Almost
unconsciously, we provide our language utterances with temporal cues, like verb tenses …

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 …

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 …