Prescriptions

Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction.

Breast milk is essential for infant health, but the transfer of xenobiotic chemicals poses significant risks. Ethical challenges in clinical trials necessitate the use of in vitro predictive models to assess chemical exposure risks in breastfeeding infants. This study introduces an explainable machine learning model to predict the risk of chemical transfer through human milk. Our novel framework i...

Evaluation of a context-aware chatbot using retrieval-augmented generation for answering clinical questions on medication-related osteonecrosis of the jaw.

The potential of large language models (LLMs) in medical applications is significant, and Retrieval-augmented generation (RAG) can address the weaknesses of these models in terms of data transparency and scientific accuracy by incorporating current scientific knowledge into responses. In this study, RAG and GPT-4 by OpenAI were applied to develop GuideGPT, a context aware chatbot integrated with a...

Machine Learning-Driven Prediction, Preparation, and Evaluation of Functional Nanomedicines Via Drug-Drug Self-Assembly.

Small molecules as nanomedicine carriers offer advantages in drug loading and preparation. Selecting effective small molecules for stable nanomedicine...

AI-powered drug discovery for neglected diseases: accelerating public health solutions in the developing world.

The emergence of artificial intelligence (AI) in drug discovery represents a transformative development in addressing neglected diseases, particularly...

Sequential recommendation via agent-based irrelevancy skipping.

Sequential Recommendation is based on modelling sequential dependencies in user interactions to produce subsequent recommendation results. However, du...

Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.

BACKGROUND: Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (E...

Toward Resolving Heterogeneous Mixtures of Nanocarriers in Drug Delivery Systems through Light Scattering and Machine Learning.

Nanocarriers (NCs) have emerged as a revolutionary approach in targeted drug delivery, promising to enhance drug efficacy and reduce toxicity through ...

Nationwide real-world implementation of AI for cancer detection in population-based mammography screening.

Artificial intelligence (AI) in mammography screening has shown promise in retrospective evaluations, but few prospective studies exist. PRAIM is an o...

Meta-Learning Enables Complex Cluster-Specific Few-Shot Binding Affinity Prediction for Protein-Protein Interactions.

Predicting protein-protein interaction (PPI) binding affinities in unseen protein complex clusters is essential for elucidating complex protein intera...

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 remains a challenge due to the complexity of drug comb...

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 preventable. Intravenous medications (including admini...

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 combination medications is a challenging task. Howe...

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

INTRODUCTION: Mental disorders, such as anxiety and depression, significantly impacted global populations in 2019 and 2020, with COVID-19 causing a su...

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 (SDSs) over the past few years, but we still do not f...

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 AI-driven drug discovery. Mainstream studies often ...

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 the second most common NDD worldwide which affects...

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 the pharmacological properties of drugs. Computatio...

Drug discovery and mechanism prediction with explainable graph neural networks.

Apprehension of drug action mechanism is paramount for drug response prediction and precision medicine. The unprecedented development of machine learn...

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 studies have found that deep learning can effectively p...

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 Prediction (DRP) is a standard procedure in pharmaceutical ...