AI Medical Compendium Topic:
Data Mining

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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...

Ease of adoption of clinical natural language processing software: An evaluation of five systems.

Journal of biomedical informatics
OBJECTIVE: In recognition of potential barriers that may inhibit the widespread adoption of biomedical software, the 2014 i2b2 Challenge introduced a special track, Track 3 - Software Usability Assessment, in order to develop a better understanding o...

Combining knowledge- and data-driven methods for de-identification of clinical narratives.

Journal of biomedical informatics
A recent promise to access unstructured clinical data from electronic health records on large-scale has revitalized the interest in automated de-identification of clinical notes, which includes the identification of mentions of Protected Health Infor...

Agile text mining for the 2014 i2b2/UTHealth Cardiac risk factors challenge.

Journal of biomedical informatics
This paper describes the use of an agile text mining platform (Linguamatics' Interactive Information Extraction Platform, I2E) to extract document-level cardiac risk factors in patient records as defined in the i2b2/UTHealth 2014 challenge. The appro...

A Robust e-Epidemiology Tool in Phenotyping Heart Failure with Differentiation for Preserved and Reduced Ejection Fraction: the Electronic Medical Records and Genomics (eMERGE) Network.

Journal of cardiovascular translational research
Identifying populations of heart failure (HF) patients is paramount to research efforts aimed at developing strategies to effectively reduce the burden of this disease. The use of electronic medical record (EMR) data for this purpose is challenging g...

Semi-supervised Learning for the BioNLP Gene Regulation Network.

BMC bioinformatics
BACKGROUND: The BioNLP Gene Regulation Task has attracted a diverse collection of submissions showcasing state-of-the-art systems. However, a principal challenge remains in obtaining a significant amount of recall. We argue that this is an important ...

Extracting biomedical events from pairs of text entities.

BMC bioinformatics
BACKGROUND: Huge amounts of electronic biomedical documents, such as molecular biology reports or genomic papers are generated daily. Nowadays, these documents are mainly available in the form of unstructured free texts, which require heavy processin...

The contribution of co-reference resolution to supervised relation detection between bacteria and biotopes entities.

BMC bioinformatics
BACKGROUND: The acquisition of knowledge about relations between bacteria and their locations (habitats and geographical locations) in short texts about bacteria, as defined in the BioNLP-ST 2013 Bacteria Biotope task, depends on the detection of co-...

A literature-driven method to calculate similarities among diseases.

Computer methods and programs in biomedicine
BACKGROUND: "Our lives are connected by a thousand invisible threads and along these sympathetic fibers, our actions run as causes and return to us as results". It is Herman Melville's famous quote describing connections among human lives. To paraphr...

Pairwise Constraint-Guided Sparse Learning for Feature Selection.

IEEE transactions on cybernetics
Feature selection aims to identify the most informative features for a compact and accurate data representation. As typical supervised feature selection methods, Lasso and its variants using L1-norm-based regularization terms have received much atten...