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Automatic Extraction and Post-coordination of Spatial Relations in Consumer Language.

To incorporate ontological concepts in natural language processing (NLP) it is often necessary to combine simple concepts into complex concepts (post-coordination). This is especially true in consumer language, where a more limited vocabulary forces consumers to utilize highly productive language that is almost impossible to pre-coordinate in an ontology. Our work focuses on recognizing an importa...

Handling Temporality of Clinical Events for Drug Safety Surveillance.

Using longitudinal data in electronic health records (EHRs) for post-marketing adverse drug event (ADE) detection allows for monitoring patients throughout their medical history. Machine learning methods have been shown to be efficient and effective in screening health records and detecting ADEs. How best to exploit historical data, as encoded by clinical events in EHRs is, however, not very well ...

Pharmacovigilance through the development of text mining and natural language processing techniques.

Gait speed using powered robotic exoskeletons after spinal cord injury: a systematic review and correlational study.

Powered robotic exoskeletons are an emerging technology of wearable orthoses that can be used as an assistive device to enable non-ambulatory individu...

Text mining for pharmacovigilance: Using machine learning for drug name recognition and drug-drug interaction extraction and classification.

Pharmacovigilance (PV) is defined by the World Health Organization as the science and activities related to the detection, assessment, understanding a...

The association between cardiorespiratory fitness and abdominal adiposity in postmenopausal, physically inactive South Asian women.

In South Asians, a unique obesity phenotype of high abdominal fat is associated with increased cardiovascular risk. Low cardiorespiratory fitness (CRF...

Extracting Biomedical Event with Dual Decomposition Integrating Word Embeddings.

Extracting biomedical event from literatures has attracted much attention recently. By now, most of the state-of-the-art systems have been based on pi...

Individual prediction of chronic motor outcome in the acute post-stroke stage: Behavioral parameters versus functional imaging.

Several neurobiological factors have been found to correlate with functional recovery after brain lesions. However, predicting the individual potentia...

On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions.

The advances achieved in Natural Language Processing make it possible to automatically mine information from electronically created documents. Many Na...

Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic region is an established therapy for advanced Parkinson's disease (PD). However, patients ...

Using swing resistance and assistance to improve gait symmetry in individuals post-stroke.

A major characteristic of hemiplegic gait observed in individuals post-stroke is spatial and temporal asymmetry, which may increase energy expenditure...

Toward a complete dataset of drug-drug interaction information from publicly available sources.

Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete sou...

Multifractal Analysis and Relevance Vector Machine-Based Automatic Seizure Detection in Intracranial EEG.

Automatic seizure detection technology is of great significance for long-term electroencephalogram (EEG) monitoring of epilepsy patients. The aim of t...

Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.

OBJECTIVE: Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utiliz...

Use of a robotic device for the rehabilitation of severe upper limb paresis in subacute stroke: exploration of patient/robot interactions and the motor recovery process.

This pioneering observational study explored the interaction between subacute stroke inpatients and a rehabilitation robot during upper limb training....

Assist-as-Needed Robot-Aided Gait Training Improves Walking Function in Individuals Following Stroke.

A novel robot-aided assist-as-needed gait training paradigm has been developed recently. This paradigm encourages subjects' active participation durin...

MIRAGE: Multi-scale Lesion-Informed Representation with Auxiliary Guidance for MRI Contrast Enhancement

Inferring contrast enhancement from one pre-contrast breast MRI slice is underdetermined: post-contrast appearance contains physiological information ...

Interpretable Peripheral Blood Cell Classification via Vision-Language Concept Bottleneck and Soft Decision Tree

Motivation: Deep learning classifiers for medical image analysis typically function as black boxes, disclosing neither the image features underlying t...

SciForma: Structure-Faithful Generation of Scientific Diagrams

Structural fidelity is essential to scientific methodology diagrams. To communicate research logic, these diagrams must faithfully render components, ...

Machine learning and data-driven models for predicting post-stroke dysphagia: a systematic review and meta-analysis

Background: Post-stroke dysphagia (PSD) contributes to aspiration, pneumonia, malnutrition, prolonged hospitalization and mortality. We evaluated the ...