We design a neural model to learn a semantic representation for clauses from graph convolution over latent representations of the subject and verb phrase.
In this work, we propose a new NLP task that aims to generate a list of problems in a patient’s daily care plan using input from the provider’s progress notes during hospitalization.
In this paper, we outline an innovative approach for augmenting the proficiency of LLMs in the realm of automated diagnosis generation, achieved through the incorporation of a medical knowledge graph (KG) and a novel graph model: Dr. Knows, inspired by the clinical diagnostic reasoning process.
The BioNLP Workshop 2023 initiated the launch of a shared task on Problem List Summarization (ProbSum) in January 2023.
Generative artificial intelligence (AI) is a promising direction for augmenting clinical diagnostic decision support and reducing diagnostic errors, a leading contributor to medical errors.
The goal of the task was to identify and prioritize diagnoses as the first steps in diagnostic decision support to find the most relevant information in long documents like daily progress notes.
The potential for clinical natural language processing (cNLP) to model diagnostic reasoning in humans with forward reasoning from data to diagnosis and potentially reduce the cognitive burden and medical error has not been investigated.
In this work, we propose a new NLP task that aims to generate a list of problems in a patient's daily care plan using input from the provider's progress notes during hospitalization.
This work introduces a hierarchical annotation schema with three stages to address clinical text understanding, clinical reasoning, and summarization.
Conclusions: The existing clinical NLP tasks cover a wide range of topics and the field will continue to grow and attract more attention from both general domain NLP and clinical informatics community.
Given a query and an untrimmed video, the temporal grounding model predicts the target interval, and the predicted video clip is fed into a video translation task by generating a simplified version of the input query.
On DeSSE, which has a more even balance of complex sentence types, our model achieves higher accuracy on the number of atomic sentences than an encoder-decoder baseline.
We present a unique dataset of student source-based argument essays to facilitate research on the relations between content, argumentation skills, and assessment.