AIMC Topic: Natural Language Processing

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Classifiers of Medical Eponymy in Scientific Texts.

Studies in health technology and informatics
Many concepts in the medical literature are named after persons. Frequent ambiguities and spelling varieties, however, complicate the automatic recognition of such eponyms with natural language processing (NLP) tools. Recently developed methods inclu...

Health-Related Content in Transformer-Based Language Models: Exploring Bias in Domain General vs. Domain Specific Training Sets.

Studies in health technology and informatics
In this communication, we demonstrate that the bias observed in domain general training sets with health-related content is not improved in domain specific health-communication corpora, contra.

TRSRD: a database for research on risky substances in tea using natural language processing and knowledge graph-based techniques.

Database : the journal of biological databases and curation
During the production and processing of tea, harmful substances are often introduced. However, they have never been systematically integrated, and it is impossible to understand the harmful substances that may be introduced during tea production and ...

Supporting the Capture of Social Needs Through Natural Language Processing.

Journal of the American Board of Family Medicine : JABFM
Social factors impact morbidity and mortality among patients. Documenting social needs in the clinical notes is currently widely done by family physicians. The unstructured format of information on social factors in electronic health records limits t...

Impact of Professional Background on Inter-Annotator Variability and Accuracy During Annotation of Clinical Notes.

Studies in health technology and informatics
BACKGROUND: The aging population's need for treatment of chronic diseases is exhibiting a marked increase in urgency, with heart failure being one of the most severe diseases in this regard. To improve outpatient care of these patients and reduce hos...

A natural language processing approach to categorise contributing factors from patient safety event reports.

BMJ health & care informatics
OBJECTIVES: The objective of this study was to explore the use of natural language processing (NLP) algorithm to categorise contributing factors from patient safety event (PSE). Contributing factors are elements in the healthcare process (eg, communi...

Natural Language Processing Methods to Empirically Explore Social Contexts and Needs in Cancer Patient Notes.

JCO clinical cancer informatics
PURPOSE: There is an unmet need to empirically explore and understand drivers of cancer disparities, particularly social determinants of health. We explored natural language processing methods to automatically and empirically extract clinical documen...

Using language models and ontology topology to perform semantic mapping of traits between biomedical datasets.

Bioinformatics (Oxford, England)
MOTIVATION: Human traits are typically represented in both the biomedical literature and large population studies as descriptive text strings. Whilst a number of ontologies exist, none of these perfectly represent the entire human phenome and exposom...

NEREL-BIO: a dataset of biomedical abstracts annotated with nested named entities.

Bioinformatics (Oxford, England)
MOTIVATION: This article describes NEREL-BIO-an annotation scheme and corpus of PubMed abstracts in Russian and smaller number of abstracts in English. NEREL-BIO extends the general domain dataset NEREL by introducing domain-specific entity types. NE...

Analysis of 'One in a Million' primary care consultation conversations using natural language processing.

BMJ health & care informatics
BACKGROUND: Modern patient electronic health records form a core part of primary care; they contain both clinical codes and free text entered by the clinician. Natural language processing (NLP) could be employed to generate these records through 'lis...