Clinical natural language processing in languages other than English: opportunities and challenges
Background Natural language processing applied to clinical text or aimed at a clinical
outcome has been thriving in recent years. This paper offers the first broad overview of …
outcome has been thriving in recent years. This paper offers the first broad overview of …
Text mining for adverse drug events: the promise, challenges, and state of the art
Text mining is the computational process of extracting meaningful information from large
amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources …
amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources …
Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning
Motivation Creating knowledge bases and ontologies is a time consuming task that relies on
manual curation. AI/NLP approaches can assist expert curators in populating these …
manual curation. AI/NLP approaches can assist expert curators in populating these …
[HTML][HTML] Deep patient: an unsupervised representation to predict the future of patients from the electronic health records
Secondary use of electronic health records (EHRs) promises to advance clinical research
and better inform clinical decision making. Challenges in summarizing and representing …
and better inform clinical decision making. Challenges in summarizing and representing …
Multi-domain clinical natural language processing with MedCAT: the medical concept annotation toolkit
Electronic health records (EHR) contain large volumes of unstructured text, requiring the
application of information extraction (IE) technologies to enable clinical analysis. We present …
application of information extraction (IE) technologies to enable clinical analysis. We present …
Deep representation learning of electronic health records to unlock patient stratification at scale
Deriving disease subtypes from electronic health records (EHRs) can guide next-generation
personalized medicine. However, challenges in summarizing and representing patient data …
personalized medicine. However, challenges in summarizing and representing patient data …
BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications
Abstract The National Center for Biomedical Ontology (NCBO) is one of the National Centers
for Biomedical Computing funded under the NIH Roadmap Initiative. Contributing to the …
for Biomedical Computing funded under the NIH Roadmap Initiative. Contributing to the …
The role of ontologies in biological and biomedical research: a functional perspective
R Hoehndorf, PN Schofield… - Briefings in …, 2015 - academic.oup.com
Ontologies are widely used in biological and biomedical research. Their success lies in their
combination of four main features present in almost all ontologies: provision of standard …
combination of four main features present in almost all ontologies: provision of standard …
Ontology-driven weak supervision for clinical entity classification in electronic health records
JA Fries, E Steinberg, S Khattar, SL Fleming… - Nature …, 2021 - nature.com
In the electronic health record, using clinical notes to identify entities such as disorders and
their temporality (eg the order of an event relative to a time index) can inform many important …
their temporality (eg the order of an event relative to a time index) can inform many important …
Matrix factorization-based data fusion for the prediction of lncRNA–disease associations
Abstract Motivation Long non-coding RNAs (lncRNAs) play crucial roles in complex disease
diagnosis, prognosis, prevention and treatment, but only a small portion of lncRNA–disease …
diagnosis, prognosis, prevention and treatment, but only a small portion of lncRNA–disease …