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Using classification models for the generation of disease-specific medications from biomedical literature and clinical data repository.

Journal of biomedical informatics
OBJECTIVE: Mining disease-specific associations from existing knowledge resources can be useful for building disease-specific ontologies and supporting knowledge-based applications. Many association mining techniques have been exploited. However, the...

Scientific Reproducibility in Biomedical Research: Provenance Metadata Ontology for Semantic Annotation of Study Description.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Scientific reproducibility is key to scientific progress as it allows the research community to build on validated results, protect patients from potentially harmful trial drugs derived from incorrect results, and reduce wastage of valuable resources...

Recognizing Question Entailment for Medical Question Answering.

AMIA ... Annual Symposium proceedings. AMIA Symposium
With the increasing heterogeneity and specialization of medical texts, automated question answering is becoming more and more challenging. In this context, answering a given medical question by retrieving similar questions that are already answered b...

Three-Class Mammogram Classification Based on Descriptive CNN Features.

BioMed research international
In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have prese...

Determining Fuzzy Membership for Sentiment Classification: A Three-Layer Sentiment Propagation Model.

PloS one
Enormous quantities of review documents exist in forums, blogs, twitter accounts, and shopping web sites. Analysis of the sentiment information hidden in these review documents is very useful for consumers and manufacturers. The sentiment orientation...

Multi-atlas and unsupervised learning approach to perirectal space segmentation in CT images.

Australasian physical & engineering sciences in medicine
Perirectal space segmentation in computed tomography images aids in quantifying radiation dose received by healthy tissues and toxicity during the course of radiation therapy treatment of the prostate. Radiation dose normalised by tissue volume facil...

A novel algorithm for ventricular arrhythmia classification using a fuzzy logic approach.

Australasian physical & engineering sciences in medicine
In the present study, it has been shown that an unnecessary implantable cardioverter-defibrillator (ICD) shock is often delivered to patients with an ambiguous ECG rhythm in the overlap zone between ventricular tachycardia (VT) and ventricular fibril...

A Novel Semi-Supervised Method of Electronic Nose for Indoor Pollution Detection Trained by M-S4VMs.

Sensors (Basel, Switzerland)
Electronic nose (E-nose), as a device intended to detect odors or flavors, has been widely used in many fields. Many labeled samples are needed to gain an ideal E-nose classification model. However, the labeled samples are not easy to obtain and ther...

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators.

PloS one
Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survival of out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrillators (AED). AED algorithms for VF-detection are customarily...

Feed-forward neural network model for hunger and satiety related VAS score prediction.

Theoretical biology & medical modelling
BACKGROUND: An artificial neural network approach was chosen to model the outcome of the complex signaling pathways in the gastro-intestinal tract and other peripheral organs that eventually produce the satiety feeling in the brain upon feeding.