AIMC Topic: Natural Language Processing

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Automatic ICD-10 classification of cancers from free-text death certificates.

International journal of medical informatics
OBJECTIVE: Death certificates provide an invaluable source for cancer mortality statistics; however, this value can only be realised if accurate, quantitative data can be extracted from certificates--an aim hampered by both the volume and variable na...

A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department.

Suicide & life-threatening behavior
What adolescents say when they think about or attempt suicide influences the medical care they receive. Mental health professionals use teenagers' words, actions, and gestures to gain insight into their emotional state and to prescribe what they beli...

A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases.

Journal of biomedical informatics
Automated phenotype identification plays a critical role in cohort selection and bioinformatics data mining. Natural Language Processing (NLP)-informed classification techniques can robustly identify phenotypes in unstructured medical notes. In this ...

Normalizing clinical terms using learned edit distance patterns.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: Variations of clinical terms are very commonly encountered in clinical texts. Normalization methods that use similarity measures or hand-coded approximation rules for matching clinical terms to standard terminologies have limited accuracy...

Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1.

Journal of biomedical informatics
The 2014 i2b2/UTHealth Natural Language Processing (NLP) shared task featured four tracks. The first of these was the de-identification track focused on identifying protected health information (PHI) in longitudinal clinical narratives. The longitudi...

Learning the Structure of Biomedical Relationships from Unstructured Text.

PLoS computational biology
The published biomedical research literature encompasses most of our understanding of how drugs interact with gene products to produce physiological responses (phenotypes). Unfortunately, this information is distributed throughout the unstructured te...

Hedge Scope Detection in Biomedical Texts: An Effective Dependency-Based Method.

PloS one
Hedge detection is used to distinguish uncertain information from facts, which is of essential importance in biomedical information extraction. The task of hedge detection is often divided into two subtasks: detecting uncertain cues and their linguis...

Using Linked Data for polarity classification of patients' experiences.

Journal of biomedical informatics
Polarity classification is the main subtask of sentiment analysis and opinion mining, well-known problems in natural language processing that have attracted increasing attention in recent years. Existing approaches mainly rely on the subjective part ...

Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2.

Journal of biomedical informatics
The second track of the 2014 i2b2/UTHealth natural language processing shared task focused on identifying medical risk factors related to Coronary Artery Disease (CAD) in the narratives of longitudinal medical records of diabetic patients. The risk f...