AI Medical Compendium Topic:
Data Mining

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Using text mining techniques to extract phenotypic information from the PhenoCHF corpus.

BMC medical informatics and decision making
BACKGROUND: Phenotypic information locked away in unstructured narrative text presents significant barriers to information accessibility, both for clinical practitioners and for computerised applications used for clinical research purposes. Text mini...

Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases.

Scientific reports
Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are s...

Interactive Cohort Identification of Sleep Disorder Patients Using Natural Language Processing and i2b2.

Applied clinical informatics
UNLABELLED: Nationwide Children's Hospital established an i2b2 (Informatics for Integrating Biology & the Bedside) application for sleep disorder cohort identification. Discrete data were gleaned from semistructured sleep study reports. The system sh...

Identifying synonymy between relational phrases using word embeddings.

Journal of biomedical informatics
Many text mining applications in the biomedical domain benefit from automatic clustering of relational phrases into synonymous groups, since it alleviates the problem of spurious mismatches caused by the diversity of natural language expressions. Mos...

Annotating risk factors for heart disease in clinical narratives for diabetic patients.

Journal of biomedical informatics
The 2014 i2b2/UTHealth natural language processing shared task featured a track focused on identifying risk factors for heart disease (specifically, Cardiac Artery Disease) in clinical narratives. For this track, we used a "light" annotation paradigm...

Clinical Documents Clustering Based on Medication/Symptom Names Using Multi-View Nonnegative Matrix Factorization.

IEEE transactions on nanobioscience
Clinical documents are rich free-text data sources containing valuable medication and symptom information, which have a great potential to improve health care. In this paper, we build an integrating system for extracting medication names and symptom ...

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...

Identification of genomic features in the classification of loss- and gain-of-function mutation.

BMC medical informatics and decision making
BACKGROUND: Alterations of a genome can lead to changes in protein functions. Through these genetic mutations, a protein can lose its native function (loss-of-function, LoF), or it can confer a new function (gain-of-function, GoF). However, when a mu...

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...

Automating the generation of lexical patterns for processing free text in clinical documents.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Many tasks in natural language processing utilize lexical pattern-matching techniques, including information extraction (IE), negation identification, and syntactic parsing. However, it is generally difficult to derive patterns that achiev...