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Exploring human activity recognition using feature level fusion of inertial and electromyography data.

Wearables are objective tools for human activity recognition (HAR). Advances in wearables enable syn...

Overcoming Major Barriers to Build Efficient Decision Support Systems in Pharmacovigilance.

Many decision support methods and systems in pharmacovigilance are built without explicitly addressi...

Named Entity Recognition in Pubmed Abstracts for Pharmacovigilance Using Deep Learning.

Methods of natural language processing associated with machine learning or deep learning can support...

A systemic review and meta-analysis of the effects of perioperative anticoagulant and antiplatelet therapy on bleeding complications in robot-assisted prostatectomy.

OBJECTIVE: Robot-assisted prostatectomy is commonly performed for the management of prostate cancer....

Automatic Deep Learning Segmentation and Quantification of Epicardial Adipose Tissue in Non-Contrast Cardiac CT scans.

An Automatic deep learning semantic segmentation (ADLS) using DeepLab-v3-plus technique is proposed ...

DeepADEMiner: a deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter.

OBJECTIVE: Research on pharmacovigilance from social media data has focused on mining adverse drug e...

[Robot-assisted nephroureterectomy requiring no robot redocking or patient repositioning: experience from a single center with 62 cases].

To examine a new technique of robot-assisted nephroureterectomy without robot reldocking or patient...

Machine learning-integrated omics for the risk and safety assessment of nanomaterials.

With the advancement in nanotechnology, we are experiencing transformation in world order with deep ...

Open Targets Platform: supporting systematic drug-target identification and prioritisation.

The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowle...

Automated Detection of Enhanced DBS Device Settings.

Continuous deep brain stimulation (DBS) of the ventral striatum (VS) is an effective treatment for s...

Deep learning for drug-drug interaction extraction from the literature: a review.

Drug-drug interactions (DDIs) are crucial for drug research and pharmacovigilance. These interaction...

Wavelet Spectral Deep-training of Convolutional Neural Networks for Accurate Identification of High-Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG of Preterm Sheep.

Early diagnosis and prognosis of babies with signs of hypoxic-ischemic encephalopathy (HIE) is curre...

Semiautomated Approach for Muscle Weakness Detection in Clinical Texts.

The automated detection of adverse events in medical records might be a cost-effective solution for ...

Machine Intelligence in Cardiovascular Medicine.

The computer science technology trend called artificial intelligence (AI) is not new. Both machine l...

Application of Augmented Intelligence for Pharmacovigilance Case Seriousness Determination.

INTRODUCTION: Identification of adverse events and determination of their seriousness ensures timely...

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 th...

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...

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 firs...

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 pharma...

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

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 on...