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Prescriptions

Latest AI and machine learning research in prescriptions for healthcare professionals.

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Showing 568-588 of 6,771 articles
Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning.

With the growing global challenge of drug abuse, there is an urgent need for rapid, accurate, and co...

Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution.

Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy rema...

ICH-PRNet: a cross-modal intracerebral haemorrhage prognostic prediction method using joint-attention interaction mechanism.

Accurately predicting intracerebral hemorrhage (ICH) prognosis is a critical and indispensable step ...

Plant and marine-derived natural products: sustainable pathways for future drug discovery and therapeutic development.

Plant- and marine-derived natural products are rich sources of bioactive compounds essential for dru...

User preference interaction fusion and swap attention graph neural network for recommender system.

Recommender systems are widely used in various applications. Knowledge graphs are increasingly used ...

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.

BACKGROUND: Fluid overload (FO) in the intensive care unit (ICU) is common, serious, and may be prev...

ComNet: A Multiview Deep Learning Model for Predicting Drug Combination Side Effects.

As combination therapy becomes more common in clinical applications, predicting adverse effects of c...

Psychotropic medications: a descriptive study of prescription trends in Tabriz, Iran, 2021-2022.

INTRODUCTION: Mental disorders, such as anxiety and depression, significantly impacted global popula...

Building for speech: designing the next-generation of social robots for audio interaction.

There have been significant advances in robotics, conversational AI, and spoken dialogue systems (SD...

A novel non-invasive murine model for rapidly testing drug activity via inhalation administration against .

The efficacy of many compounds against is often limited when administered via conventional oral or ...

MIFS: An adaptive multipath information fused self-supervised framework for drug discovery.

The production of expressive molecular representations with scarce labeled data is challenging for A...

Drug repositioning for Parkinson's disease: An emphasis on artificial intelligence approaches.

Parkinson's disease (PD) is one of the most incapacitating neurodegenerative diseases (NDDs). PD is ...

Neighborhood Topology-Aware Knowledge Graph Learning and Microbial Preference Inferring for Drug-Microbe Association Prediction.

The human microbiota may influence the effectiveness of drug therapy by activating or inactivating t...

Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods.

The integration of drug molecular representations into predictive models for Drug Response Predictio...

Deep learning-based discovery of compounds for blood pressure lowering effects.

The hypotensive side effects caused by drugs during their use have been a vexing issue. Recent studi...

Drug discovery and mechanism prediction with explainable graph neural networks.

Apprehension of drug action mechanism is paramount for drug response prediction and precision medici...

Spatially-Constrained and -Unconstrained Bi-Graph Interaction Network for Multi-Organ Pathology Image Classification.

In computational pathology, graphs have shown to be promising for pathology image analysis. There ex...

Deep Learning for the Accurate Prediction of Triggered Drug Delivery.

The need to mitigate the adverse effects of chemotherapy has driven the exploration of innovative dr...

Vision Sensor for Automatic Recognition of Human Activities via Hybrid Features and Multi-Class Support Vector Machine.

Over recent years, automated Human Activity Recognition (HAR) has been an area of concern for many r...

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