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Medical Informatics

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Entity linking for biomedical literature.

BMC medical informatics and decision making
BACKGROUND: The Entity Linking (EL) task links entity mentions from an unstructured document to entities in a knowledge base. Although this problem is well-studied in news and social media, this problem has not received much attention in the life sci...

Parsing clinical text: how good are the state-of-the-art parsers?

BMC medical informatics and decision making
BACKGROUND: Parsing, which generates a syntactic structure of a sentence (a parse tree), is a critical component of natural language processing (NLP) research in any domain including medicine. Although parsers developed in the general English domain,...

A formal concept analysis and semantic query expansion cooperation to refine health outcomes of interest.

BMC medical informatics and decision making
BACKGROUND: Electronic Health Records (EHRs) are frequently used by clinicians and researchers to search for, extract, and analyze groups of patients by defining Health Outcome of Interests (HOI). The definition of an HOI is generally considered a co...

Injury narrative text classification using factorization model.

BMC medical informatics and decision making
Narrative text is a useful way of identifying injury circumstances from the routine emergency department data collections. Automatically classifying narratives based on machine learning techniques is a promising technique, which can consequently redu...

Bilingual term alignment from comparable corpora in English discharge summary and Chinese discharge summary.

BMC bioinformatics
BACKGROUND: Electronic medical record (EMR) systems have become widely used throughout the world to improve the quality of healthcare and the efficiency of hospital services. A bilingual medical lexicon of Chinese and English is needed to meet the de...

Sentiment analysis in medical settings: New opportunities and challenges.

Artificial intelligence in medicine
OBJECTIVE: Clinical documents reflect a patient's health status in terms of observations and contain objective information such as descriptions of examination results, diagnoses and interventions. To evaluate this information properly, assessing posi...

Abstraction networks for terminologies: Supporting management of "big knowledge".

Artificial intelligence in medicine
OBJECTIVE: Terminologies and terminological systems have assumed important roles in many medical information processing environments, giving rise to the "big knowledge" challenge when terminological content comprises tens of thousands to millions of ...

A semi-supervised learning framework for biomedical event extraction based on hidden topics.

Artificial intelligence in medicine
OBJECTIVES: Scientists have devoted decades of efforts to understanding the interaction between proteins or RNA production. The information might empower the current knowledge on drug reactions or the development of certain diseases. Nevertheless, du...

Automatic negation detection in narrative pathology reports.

Artificial intelligence in medicine
OBJECTIVE: To detect negations of medical entities in free-text pathology reports with different approaches, and evaluate their performances.

Application of a hybrid method combining grey model and back propagation artificial neural networks to forecast hepatitis B in china.

Computational and mathematical methods in medicine
Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the ...