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Prescriptions

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

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HormoNet: a deep learning approach for hormone-drug interaction prediction.

Several experimental evidences have shown that the human endogenous hormones can interact with drugs...

Recovery of the spatially-variant deformations in dual-panel PET reconstructions using deep-learning.

Dual panel PET systems, such as Breast-PET (B-PET) scanner, exhibit strong asymmetric and anisotropi...

Severe Hyperkalemia During a Robot-Assisted Total Radical Prostatectomy in a Patient with Stage 3a Chronic Kidney Disease: A Case Report.

A 63-year-old man with stage 3a chronic kidney disease (CKD) and mild hyperkalemia was scheduled for...

AI-enhanced biomedical micro/nanorobots in microfluidics.

Human beings encompass sophisticated microcirculation and microenvironments, incorporating a broad s...

Exploring the Use of Socially Assistive Robots Among Socially Isolated Korean American Older Adults.

This pilot study explored whether a socially assistive robot (SAR) would have positive effects on Ko...

Modeling 5-FU-Induced Chemotherapy Selection of a Drug-Resistant Cancer Stem Cell Subpopulation.

(1) Background: Cancer stem cells (CSCs) are a subpopulation of cells in a tumor that can self-regen...

Warfarin-A natural anticoagulant: A review of research trends for precision medication.

BACKGROUND: Warfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable ...

Prescription eyeglasses as a forensic physical evidence: Prediction of age based on refractive error measures using machine learning algorithm.

Refractive errors (RE) are commonly reported visual impairment problems worldwide. Previous clinical...

Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction.

Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction bet...

A stroke prediction framework using explainable ensemble learning.

The death of brain cells occurs when blood flow to a particular area of the brain is abruptly cut of...

SynerGNet: A Graph Neural Network Model to Predict Anticancer Drug Synergy.

Drug combination therapy shows promise in cancer treatment by addressing drug resistance, reducing t...

Enhancing drug discovery in schizophrenia: a deep learning approach for accurate drug-target interaction prediction - DrugSchizoNet.

Drug discovery relies on the precise prognosis of drug-target interactions (DTI). Due to their abili...

Transforming drug development with synthetic biology and AI.

The COVID-19 pandemic has thrust RNA as a platform for drug development into the spotlight. However,...

Screening oral drugs for their interactions with the intestinal transportome via porcine tissue explants and machine learning.

In vitro systems that accurately model in vivo conditions in the gastrointestinal tract may aid the ...

SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains.

Modern multiomic technologies can generate deep multiscale profiles. However, differences in data mo...

Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects.

Medicinal chemistry and drug design efforts can be assisted by machine learning (ML) models that rel...

From CySkin to ProxySKIN: Design, Implementation and Testing of a Multi-Modal Robotic Skin for Human-Robot Interaction.

The Industry 5.0 paradigm has a human-centered vision of the industrial scenario and foresees a clos...

Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events.

Drug-to-drug interaction (DDIs) occurs when a patient consumes multiple drugs. Therefore, it is poss...

Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.

Drug discovery plays a critical role in advancing human health by developing new medications and tre...

Visualizing Clinical Data Retrieval and Curation in Multimodal Healthcare AI Research: A Technical Note on RIL-workflow.

Curating and integrating data from sources are bottlenecks to procuring robust training datasets for...

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