2 research outputs found
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
To combat COVID-19, both clinicians and scientists need to digest the vast
amount of relevant biomedical knowledge in literature to understand the disease
mechanism and the related biological functions. We have developed a novel and
comprehensive knowledge discovery framework, \textbf{COVID-KG} to extract
fine-grained multimedia knowledge elements (entities, relations and events)
from scientific literature. We then exploit the constructed multimedia
knowledge graphs (KGs) for question answering and report generation, using drug
repurposing as a case study. Our framework also provides detailed contextual
sentences, subfigures and knowledge subgraphs as evidence. All of the data,
KGs, reports, resources and shared services are publicly available.Comment: 11 pages, submitted to ACL 2020 Workshop on Natural Language
Processing for COVID-19 (NLP-COVID), for resources see
http://blender.cs.illinois.edu/covid19/, for video see
http://159.89.180.81/demo/covid/Covid-KG_DemoVideo.mp
Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review
Electronic health records (EHRs), digital collections of patient healthcare
events and observations, are ubiquitous in medicine and critical to healthcare
delivery, operations, and research. Despite this central role, EHRs are
notoriously difficult to process automatically. Well over half of the
information stored within EHRs is in the form of unstructured text (e.g.
provider notes, operation reports) and remains largely untapped for secondary
use. Recently, however, newer neural network and deep learning approaches to
Natural Language Processing (NLP) have made considerable advances,
outperforming traditional statistical and rule-based systems on a variety of
tasks. In this survey paper, we summarize current neural NLP methods for EHR
applications. We focus on a broad scope of tasks, namely, classification and
prediction, word embeddings, extraction, generation, and other topics such as
question answering, phenotyping, knowledge graphs, medical dialogue,
multilinguality, interpretability, etc.Comment: 33 pages, 11 figure