Product Alert

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome.

State-of-the-art machine learning (ML) artificial intelligence methods are increasingly leveraged in clinical predictive modeling to provide clinical decision support systems to physicians. Modern ML approaches such as artificial neural networks (ANNs) and tree boosting often perform better than more traditional methods like logistic regression. On the other hand, these modern methods yield a limi...

Cloud-based ECG monitoring using event-driven ECG acquisition and machine learning techniques.

An approach is proposed for the detection of chronic heart disorders from the electrocardiogram (ECG) signals. It utilizes an intelligent event-driven ECG signal acquisition system to achieve a real-time compression and effective signal processing and transmission. The experimental results show that grace of event-driven nature an overall 2.6 times compression and bandwidth utilization gain is att...

Simulating Small Neural Circuits with a Discrete Computational Model.

Simulations of neural activity are commonly based on differential equations. We address the question what can be achieved with a simplified discrete m...

Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study.

OBJECTIVES: Pneumothorax is the most common and potentially life-threatening complication arising from percutaneous lung biopsy. We evaluated the perf...

Predicting Adverse Drug-Drug Interactions with Neural Embedding of Semantic Predications.

The identification of drug-drug interactions (DDIs) is important for patient safety; yet, compared to other pharmacovigilance work, a limited amount o...

Leveraging Contextual Information in Extracting Long Distance Relations from Clinical Notes.

Relation extraction from biomedical text is important for clinical decision support applications. In post-marketing pharmacovigilance, for example, Ad...

An Application of Machine Learning in Pharmacovigilance: Estimating Likely Patient Genotype From Phenotypical Manifestations of Fluoropyrimidine Toxicity.

Dihydropyrimidine dehydrogenase (DPD)-deficient patients might only become aware of their genotype after exposure to dihydropyrimidines, if testing is...

Convolutional neural network enhancement of fast-scan low-dose cone-beam CT images for head and neck radiotherapy.

To improve image quality and CT number accuracy of fast-scan low-dose cone-beam computed tomography (CBCT) through a deep-learning convolutional neura...

Artificial intelligence in pharmacovigilance: Practical utility.

Endocannabinoid degradation inhibitors ameliorate neuronal and synaptic alterations following traumatic brain injury.

Our previous work showed that lateral fluid percussion injury to the sensorimotor cortex (SMC) of anesthetized rats increased neuronal synaptic hypere...

Training and Validation of Deep Neural Networks for the Prediction of 90-Day Post-Liver Transplant Mortality Using UNOS Registry Data.

Prediction models of post-liver transplant mortality are crucial so that donor organs are not allocated to recipients with unreasonably high probabili...

Utilizing Advanced Technologies to Augment Pharmacovigilance Systems: Challenges and Opportunities.

There are significant challenges and opportunities in deploying and utilizing advanced information technology (IT) within pharmacovigilance (PV) syste...

Effects of post-weaning supplementation of immunomodulatory feed ingredient on body weight and cortisol concentrations in program-fed beef heifers.

The objective of this study was to determine the effects of an immunomodulatory feed ingredient during post-weaning on growth and cortisol concentrati...

Intracatheter Tissue Plasminogen Activator for Chronic Subdural Hematomas after Failed Bedside Twist Drill Craniostomy: A Retrospective Review.

Introduction Chronic subdural hematomas (cSDH) are common in neurosurgery with various symptoms and significant morbidity and mortality. Treatment var...

Predicting the occurrence of surgical site infections using text mining and machine learning.

In this study we propose the use of text mining and machine learning methods to predict and detect Surgical Site Infections (SSIs) using textual descr...

[Predicting atrial fibrillation through a sinus-rhythm electrocardiogram; useful or not?].

In patients with cryptogenic stroke, the detection of atrial fibrillation (AF) is important, since it is an indication for the prescription of oral an...

Development of artificial neural network model for prediction of post-streptococcus mutans in dental caries.

BACKGROUND AND OBJECTIVE: Streptococcus mutans is the primary initiator and most common organism associated with dental caries. Prediction of post-Str...

Comparative analysis on Facebook post interaction using DNN, ELM and LSTM.

This study presents a novel research approach to predict user interaction for social media post using machine learning algorithms. The posts are conve...

Association of polymorphisms in , , and with post-induction infliximab trough level in Crohn's disease patients.

BACKGROUND: Trough levels of the post-induction serum infliximab (IFX) are associated with short-term and long-term responses of Crohn's disease patie...

A Multi-Organ Nucleus Segmentation Challenge.

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital path...