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Association between Serum Vitamin D Concentration Status and Matrix Metalloproteinase-9 in Patients Undergoing Elective Percutaneous Coronary Intervention.

Cardiovascular diseases (CVD) have become increasingly life-threatening during recent decades. Several studies have shown that matrix metalloproteinase-9 (MMP-9) plays an important role in the process of atherosclerosis and heart remodeling. On the other hand, Vitamin D deficiency has been recognized as a risk factor for CVD. According to the prevalence of vitamin D deficiency in our country, Iran...

Medication Adherence and Stroke Prevention: What Real World Data Tells Us.

Medication adherence, by definition,(2) is "the extent to which a patient acts in accordance with the prescribed interval and dose of a dosing regimen." Poor medication adherence can interfere with the ability to treat many diseases, leading to greater complications and a lower quality of life.(3) In this issue of Acta Neurologica Taiwanica, Chen et al.(4) presented the inverse association between...

Predicting post-experiment fatigue among healthy young adults: Random forest regression analysis.

The current study utilized a random forest regression analysis to predict post-experiment fatigue in a sample of 212 healthy participants (mean age = ...

Prediction of Personal Experience Tweets of Medication Use via Contextual Word Representations.

Continuous monitoring the safe use of medication is an important task in pharmacovigilance. The first-hand experiences of medication effects come from...

Reply to comment on: "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts".

We appreciate the detailed review provided by Magge et al1 of our article, "Deep learning for pharmacovigilance: recurrent neural network architecture...

Comment on: "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts".

Soft Robotic Bilateral Hand Rehabilitation System for Fine Motor Learning.

This paper presents the development of a pneumatically actuated soft robotic based bilateral therapy system for hand rehabilitation in post-stroke pat...

gazeNet: End-to-end eye-movement event detection with deep neural networks.

Existing event detection algorithms for eye-movement data almost exclusively rely on thresholding one or more hand-crafted signal features, each compu...

Artificial Intelligence and the Future of the Drug Safety Professional.

The healthcare industry, and specifically the pharmacovigilance industry, recognizes the need to support the increasing amount of data received from i...

Artificial Intelligence Within Pharmacovigilance: A Means to Identify Cognitive Services and the Framework for Their Validation.

INTRODUCTION: Pharmacovigilance (PV) detects, assesses, and prevents adverse events (AEs) and other drug-related problems by collecting, evaluating, a...

MADEx: A System for Detecting Medications, Adverse Drug Events, and Their Relations from Clinical Notes.

INTRODUCTION: Early detection of adverse drug events (ADEs) from electronic health records is an important, challenging task to support pharmacovigila...

Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding.

INTRODUCTION: Adverse drug event (ADE) detection is a vital step towards effective pharmacovigilance and prevention of future incidents caused by pote...

Adverse Drug Events Detection in Clinical Notes by Jointly Modeling Entities and Relations Using Neural Networks.

BACKGROUND AND SIGNIFICANCE: Adverse drug events (ADEs) occur in approximately 2-5% of hospitalized patients, often resulting in poor outcomes or even...

Efficacy, chondrotoxicity and plasma concentrations of tramadol following intra-articular administration in horses undergoing arthroscopy: preliminary findings.

Intra-articular administration of analgesics is performed to ensure good perioperative pain management avoiding undesirable systemic effects. To evalu...

Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.

OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving flow to essential arteries. Incomplete occlusion e...

A chronological pharmacovigilance network analytics approach for predicting adverse drug events.

OBJECTIVES: This study extends prior research by combining a chronological pharmacovigilance network approach with machine-learning (ML) techniques to...

Effectiveness of a single session of dual-transcranial direct current stimulation in combination with upper limb robotic-assisted rehabilitation in chronic stroke patients: a randomized, double-blind, cross-over study.

The impact of transcranial direct current stimulation (tDCS) is controversial in the neurorehabilitation literature. It has been suggested that tDCS s...

Post hoc support vector machine learning for impedimetric biosensors based on weak protein-ligand interactions.

Impedimetric biosensors for measuring small molecules based on weak/transient interactions between bioreceptors and target analytes are a challenge fo...

Drug-drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths.

MOTIVATION: Adverse events resulting from drug-drug interactions (DDI) pose a serious health issue. The ability to automatically extract DDIs describe...

Using machine learning to detect events in eye-tracking data.

Event detection is a challenging stage in eye movement data analysis. A major drawback of current event detection methods is that parameters have to b...