AIMC Topic:
Data Mining

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Predicting Future Cardiovascular Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data.

Circulation. Cardiovascular quality and outcomes
BACKGROUND: Patients with peripheral artery disease (PAD) are at risk of major adverse cardiac and cerebrovascular events. There are no readily available risk scores that can accurately identify which patients are most likely to sustain an event, mak...

Clinical Value of Predicting Individual Treatment Effects for Intensive Blood Pressure Therapy.

Circulation. Cardiovascular quality and outcomes
BACKGROUND: The absolute risk reduction (ARR) in cardiovascular events from therapy is generally assumed to be proportional to baseline risk-such that high-risk patients benefit most. Yet newer analyses have proposed using randomized trial data to de...

Classifying relations in clinical narratives using segment graph convolutional and recurrent neural networks (Seg-GCRNs).

Journal of the American Medical Informatics Association : JAMIA
We propose to use segment graph convolutional and recurrent neural networks (Seg-GCRNs), which use only word embedding and sentence syntactic dependencies, to classify relations from clinical notes without manual feature engineering. In this study, t...

A Data Mining Approach to Diagnose Cancer for Therapeutic Decision Making.

Alternative therapies in health and medicine
BACKGROUND: With the increase in population, there is a rise in number of cancer cases starting from young children to old people. The uncommon cancers are generally sporadic and there are no pre-defined techniques/tools for the diagnosis. Identifyin...

LNTP-MDBN: Big Data Integrated Learning Framework for Heterogeneous Image Set Classification.

Current medical imaging reviews
BACKGROUND: With the explosive growth of global data, the term Big Data describes the enormous size of dataset through the detailed analysis. The big data analytics revealed the hidden patterns and secret correlations among the values. The major chal...

A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning.

Database : the journal of biological databases and curation
The automatic extraction of meaningful relations from biomedical literature or clinical records is crucial in various biomedical applications. Most of the current deep learning approaches for medical relation extraction require large-scale training d...

Machine learning approach to literature mining for the genetics of complex diseases.

Database : the journal of biological databases and curation
To generate a parsimonious gene set for understanding the mechanisms underlying complex diseases, we reasoned it was necessary to combine the curation of public literature, review of experimental databases and interpolation of pathway-associated gene...

Evaluation of an automatic article selection method for timelier updates of the Comet Core Outcome Set database.

Database : the journal of biological databases and curation
Curated databases of scientific literature play an important role in helping researchers find relevant literature, but populating such databases is a labour intensive and time-consuming process. One such database is the freely accessible Comet Core O...

Extraction of chemical-protein interactions from the literature using neural networks and narrow instance representation.

Database : the journal of biological databases and curation
The scientific literature contains large amounts of information on genes, proteins, chemicals and their interactions. Extraction and integration of this information in curated knowledge bases help researchers support their experimental results, leadi...