Prescriptions

Prediction of adverse drug reactions using drug convolutional neural networks.

Prediction of Adverse Drug Reactions (ADRs) has been an important aspect of Pharmacovigilance because of its impact in the pharma industry. The standard process of introduction of a new drug into a market involves a lot of clinical trials and tests. This is a tedious and time consuming process and also involves a lot of monetary resources. The faster approval of a drug helps the patients who are i...

Bidirectional Interaction Network for Person Re-Identification.

Person re-identification (ReID) task aims to retrieve the same person across multiple spatially disjoint camera views. Due to huge image changes caused by various factors such as posture variation and illumination transformation, images of different persons may share the more similar appearances than images of the same one. Learning discriminative representations to distinguish details of differen...

Improving Global Healthcare and Reducing Costs Using Second-Generation Artificial Intelligence-Based Digital Pills: A Market Disruptor.

Improving global health requires making current and future drugs more effective and affordable. While healthcare systems around the world are faced w...

Recent Progress in Flexible Microstructural Pressure Sensors toward Human-Machine Interaction and Healthcare Applications.

With the rapid growth of artificial intelligence, wearable electronic devices have caught intensive research interest recently. Flexible sensors, as t...

Artificial Intelligence in Pharmacovigilance: Scoping Points to Consider.

Artificial intelligence (AI), a highly interdisciplinary science, is an increasing presence in pharmacovigilance (PV). A better understanding of the s...

Automatic Construction of Chinese Herbal Prescriptions From Tongue Images Using CNNs and Auxiliary Latent Therapy Topics.

The tongue image provides important physical information of humans. It is of great importance for diagnoses and treatments in clinical medicine. Herba...

Intelligent automated drug administration and therapy: future of healthcare.

In the twenty-first century, the collaboration of control engineering and the healthcare sector has matured to some extent; however, the future will h...

ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of Mutations.

The ELASPIC web server allows users to evaluate the effect of mutations on protein folding and protein-protein interaction on a proteome-wide scale. I...

Retrospective analysis of the effect on interval cancer rate of adding an artificial intelligence algorithm to the reading process for two-dimensional full-field digital mammography.

Interval cancers are a commonly seen problem in organized breast cancer screening programs and their rate is measured for quality assurance. Artificia...

A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning.

The new type of corona virus (SARS-COV-2) emerging in Wuhan, China has spread rapidly to the world and has become a pandemic. In addition to having a ...

Schizotypy in Parkinson's disease predicts dopamine-associated psychosis.

Psychosis is the most common neuropsychiatric side-effect of dopaminergic therapy in Parkinson's disease (PD). It is still unknown which factors deter...

Relationship Development with Humanoid Social Robots: Applying Interpersonal Theories to Human-Robot Interaction.

(HSRs) are human-made technologies that can take physical or digital form, resemble people in form or behavior to some degree, and are designed to in...

Learning Spatiotemporal Features for Esophageal Abnormality Detection From Endoscopic Videos.

Esophageal cancer is categorized as a type of disease with a high mortality rate. Early detection of esophageal abnormalities (i.e. precancerous and e...

Multi-objective optimization methods in novel drug design.

: In multi-objective drug design, optimization gains importance, being upgraded to a discipline that attracts its own research. Current strategies are...

Systems Approach to Pathogenic Mechanism of Type 2 Diabetes and Drug Discovery Design Based on Deep Learning and Drug Design Specifications.

In this study, we proposed a systems biology approach to investigate the pathogenic mechanism for identifying significant biomarkers as drug targets a...

Applications of artificial intelligence in drug development using real-world data.

The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development. RWD can generate important...

Repurposing potential of FDA-approved and investigational drugs for COVID-19 targeting SARS-CoV-2 spike and main protease and validation by machine learning algorithm.

The present study aimed to assess the repurposing potential of existing antiviral drug candidates (FDA-approved and investigational) against SARS-CoV-...

Assessing Drug Development Risk Using Big Data and Machine Learning.

Identifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, characterizing drug development risk,...

Data-driven dose calculation algorithm based on deep U-Net.

Accurate and efficient dose calculation is an important prerequisite to ensure the success of radiation therapy. However, all the dose calculation alg...

Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet.

Although artificial intelligence (AI) has had a profound impact on areas such as image recognition, comparable advances in drug discovery are rare. Th...