Prescriptions

Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics.

Pharmaceutical industry and the art and science of drug development are sorely in need of novel transformative technologies in the current age of digital health and artificial intelligence (AI). Often described as game-changing technologies, AI and machine learning algorithms have slowly but surely begun to revolutionize pharmaceutical industry and drug development over the past 5 years. In this e...

Review of outcome measures in PARO robot intervention studies for dementia care.

The aim of this study was to describe interventions for PARO, as well as the outcomes evaluated and found following use of PARO, and to identify outcome measures in PARO intervention studies for older adults with dementia. Multiple databases (Web of Science, PubMed, Cumulative Index to Nursing and Allied Health Literature, EMBASE, Cochrane, and Scopus) were searched and eight studies were included...

Label-Free Estimation of Therapeutic Efficacy on 3D Cancer Spheres Using Convolutional Neural Network Image Analysis.

Despite recent advances in cancer treatment, developing better therapeutic reagents remains an essential task for oncologists. To accurately character...

RedMed: Extending drug lexicons for social media applications.

Social media has been identified as a promising potential source of information for pharmacovigilance. The adoption of social media data has been hind...

PARS, a system combining semantic technologies with multiple criteria decision aiding for supporting antibiotic prescriptions.

OBJECTIVE: Motivated by the well documented worldwide spread of adverse drug events, as well as the increased danger of antibiotic resistance (caused ...

Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics.

There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests th...

Generic Isolated Cell Image Generator.

Building automated cancer screening systems based on image analysis is currently a hot topic in computer vision and medical imaging community. One of ...

GSIAR: gene-subcategory interaction-based improved deep representation learning for breast cancer subcategorical analysis using gene expression, applicable for precision medicine.

Tumor subclass detection and diagnosis is inevitable requirement for personalized medical treatment and refinement of the effects that the somatic cel...

Fast and Accurate Bacterial Species Identification in Urine Specimens Using LC-MS/MS Mass Spectrometry and Machine Learning.

Fast identification of microbial species in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the presc...

An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as an Example.

In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the dr...

Making Sense of Pharmacovigilance and Drug Adverse Event Reporting: Comparative Similarity Association Analysis Using AI Machine Learning Algorithms in Dogs and Cats.

Drug-associated adverse events cause approximately 30 billion dollars a year of added health care expense, along with negative health outcomes includi...

Extracting drug-drug interactions with hybrid bidirectional gated recurrent unit and graph convolutional network.

Drug-drug interactions are critical in studying drug side effects. Thus, quickly and accurately identifying the relationship between drugs is necessar...

Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach.

Named Entity Recognition (NER) in the healthcare domain involves identifying and categorizing disease, drugs, and symptoms for biosurveillance, extrac...

Multilevel Features for Sensor-Based Assessment of Motor Fluctuation in Parkinson's Disease Subjects.

Motor fluctuations are a frequent complication in patients with Parkinson's disease (PD) where the response to medication fluctuates between ON states...

Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning.

Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance...

Latticed Channel Model of Touchable Communication Over Capillary Microcirculation Network.

Recent progress on bioresorbable and bio-compatible miniature systems provides prospects for developing novel nanorobots operating inside the human bo...

Multi-Objective Optimization for Personalized Prediction of Venous Thromboembolism in Ovarian Cancer Patients.

Thrombotic events are one of the leading causes of mortality and morbidity related to cancer, with ovarian cancer having one of the highest incidence ...

Fusing Object Information and Inertial Data for Activity Recognition.

In the field of pervasive computing, wearable devices have been widely used for recognizing human activities. One important area in this research is t...

A two-stage deep learning approach for extracting entities and relationships from medical texts.

This work presents a two-stage deep learning system for Named Entity Recognition (NER) and Relation Extraction (RE) from medical texts. These tasks ar...

Big Data and Artificial Intelligence Modeling for Drug Discovery.

Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the devel...