Prescriptions

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 of research has been conducted in this space. Recent work has successfully applied a method of deriving distributed vector representations from structured biomedical knowledge, known as Embedding of Semantic Predications (ESP), to the problem of predi...

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, Adverse Drug Events (ADE) relate medical problems to the drugs that caused them and were the focus of two recent shared challenges. While good results were reported, there was a room for improvement. Here, we studied two new improved methods for relati...

Machine Learning Based Opioid Overdose Prediction Using Electronic Health Records.

Opioid addiction in the United States has come to national attention as opioid overdose (OD) related deaths have risen at alarming rates. Combating op...

Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?

In recent years, protein-ligand interaction scoring functions derived through machine-learning are repeatedly reported to outperform conventional scor...

Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data.

Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately iden...

The Effect of Using PARO for People Living With Dementia and Chronic Pain: A Pilot Randomized Controlled Trial.

OBJECTIVES: To evaluate the effect of interaction with a robotic seal (PARO) on pain and behavioral and psychological symptoms of people with dementia...

Broad-Spectrum Profiling of Drug Safety via Learning Complex Network.

Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a rep...

A Scalable Smartwatch-Based Medication Intake Detection System Using Distributed Machine Learning.

Poor Medication adherence causes significant economic impact resulting in hospital readmission, hospital visits and other healthcare costs. The author...

A neural network method to predict task- and step-specific ground reaction force magnitudes from trunk accelerations during running activities.

Prediction of ground reaction force (GRF) magnitudes during running-based sports has several important applications, including optimal load prescripti...

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...

Prediction of Side Effects Using Comprehensive Similarity Measures.

Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction meth...

Predictability of drug-induced liver injury by machine learning.

BACKGROUND: Drug-induced liver injury (DILI) is a major concern in drug development, as hepatotoxicity may not be apparent at early stages but can lea...

A Machine Learning Approach for Drug-target Interaction Prediction using Wrapper Feature Selection and Class Balancing.

Drug-Target interaction (DTI) plays a crucial role in drug discovery, drug repositioning and understanding the drug side effects which helps to identi...

Drug-target interaction prediction with tree-ensemble learning and output space reconstruction.

BACKGROUND: Computational prediction of drug-target interactions (DTI) is vital for drug discovery. The experimental identification of interactions be...

Tip-Over Stability Analysis of a Pelvic Support Walking Robot.

Discussed in this paper is the tip-over stability analysis of a pelvic support walking robot. To improve the activities of daily living (ADL) in hemip...

RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance.

Cancer is one of the most difficult diseases to treat owing to the drug resistance of tumour cells. Recent studies have revealed that drug responses a...

Unsupervised Domain Adaptation to Classify Medical Images Using Zero-Bias Convolutional Auto-Encoders and Context-Based Feature Augmentation.

The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale labelled training d...

LFP-Net: A deep learning framework to recognize human behavioral activities using brain STN-LFP signals.

BACKGROUND: Recognition of human behavioral activities using local field potential (LFP) signals recorded from the Subthalamic Nuclei (STN) has applic...

Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model.

Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is co...

Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: A systematic review.

There is growing interest in the potential of artificial intelligence to support decision-making in health and social care settings. There is, however...