Background With the rapid adoption of electronic health records (EHRs), it is desirable to
harvest information and knowledge from EHRs to support automated systems at the point of …

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 We sought to use natural language processing to develop a suite of language
models to capture key symptoms of severe mental illness (SMI) from clinical text, to facilitate …

MetaMap is a widely used named entity recognition tool that identifies concepts from the
Unified Medical Language System Metathesaurus in text. This study presents MetaMap Lite …

To reduce adverse drug events (ADEs), hospitals need a system to support them in
monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing …

Abstract Biomedical Entity Linking (BEL) is the task of mapping of spans of text within
biomedical documents to normalized, unique identifiers within an ontology. This is an …

Background: Natural Language Processing (NLP) methods are increasingly being utilized to
mine knowledge from unstructured health-related texts. Recent advances in noisy text …

The abundance and unstructured nature of biomedical texts, be it clinical or research
content, impose significant challenges for the effective and efficient use of information and …

Polygenic diseases, which are genetic disorders caused by the combined action of multiple
genes, pose unique and significant challenges for the diagnosis and management of …

Medical knowledge bases (KBs), distilled from biomedical literature and regulatory actions,
are expected to provide high-quality information to facilitate clinical decision making. Entity …