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NER is the process of identifying and classifying key information (entities) in text, such as diseases, drugs, and genes. In healthcare, NER can be used to extract patient information from clinical notes for clinical decision support and research.

Topic modeling is used to discover the latent topics within large volumes of biomedical literature, which can assist in trend analysis and the identification of research gaps in medical science.

Corpus annotation involves manually tagging text with relevant information such as entities and their attributes, which forms the groundwork for training supervised NLP models in biomedicine.

This process resolves ambiguities in entity names to unique identifiers in text, helping disambiguate drugs and genes with the same names in healthcare records for improved patient care and data analysis.

Information summarization automatically generates condensed versions of biomedical texts, helping healthcare professionals swiftly understand patient information or research findings.

Co-occurrence analysis detects when pairs or groups of entities appear together within texts, which can reveal insights about potential associations in biomedical research, such as side effects or comorbidities.

Semantic similarity measures the closeness of meaning between textual units using ontologies or vector space representations, facilitating literature mapping and curation activities in biomedicine.

Relation extraction involves identifying relationships between entities within the text, such as drug-drug interactions or gene-disease associations. This is important for constructing knowledge graphs and supporting decision-making in precision medicine.

In the biomedical field, sentiment analysis can be used to gauge patient satisfaction from survey responses or social media, which is valuable for hospital administration and public health monitoring.

This method uses biomedical ontologies to enhance the retrieval of information by capturing the meaning and relationships inherent in the biomedical data, aiding in more precise literature searches for researchers.

Concept normalization maps entities to standardized identifiers in ontologies, which helps in unifying different terms for the same concept. This is crucial for integrating and comparing data across various sources in biomedical research.