Prescriptions

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 drug delivery approaches. One emerging trend in cancer treatment is the utilization of Drug Delivery Systems (DDSs), facilitated by nanotechnology. Nanoparticles, ranging from 1 nm to 1000 nm, act as carriers for chemotherapeutic agents, enabling precise drug delivery. The triggered release of these a...

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 exist various graph structures that can discover differing features of pathology images. However, the combination and interaction between differing graph structures have not been fully studied and utilized for pathology image analysis. In this study, we propose a parallel, bi-graph neural network, des...

Analysis of User-Generated Posts on Social Media of Adjuvant Analgesics: A Machine Learning Study.

Antiepileptics and antidepressants are frequently prescribed for chronic pain, but their efficacy and potential adverse effects raise concerns, inclu...

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 researchers due to its widespread application in su...

A machine learning-based clinical predictive tool to identify patients at high risk of medication errors.

A medication error is an inadvertent failure in the drug therapy process that can cause serious harm to patients by increasing morbidity and mortality...

Predicting patients' sentiments about medications using artificial intelligence techniques.

The increasing development of technology has led to the increase of digital data in various fields, such as medication-related texts. Sentiment Analys...

Applying AI to Structured Real-World Data for Pharmacovigilance Purposes: Scoping Review.

BACKGROUND: Artificial intelligence (AI) applied to real-world data (RWD; eg, electronic health care records) has been identified as a potentially pro...

CPI-GGS: A deep learning model for predicting compound-protein interaction based on graphs and sequences.

BACKGROUND: Compound-protein interaction (CPI) is essential to drug discovery and design, where traditional methods are often costly and have low succ...

Effect of Photodynamic Therapy With Two Different Photosensitizers on the Viability of Human Dental Pulp Stem Cells.

Photodynamic therapy(PDT)is a minimally invasive technique increasingly used in dentistry for its antimicrobial properties. This research aimed to ev...

Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics.

Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique...

Adoption of Digital Therapeutics in Europe.

Digital therapeutics (DTx) are an emerging medical therapy comprising evidence-based interventions that are regulatory approved for patient use, or ar...

Hope for the best prepare for the worst: acute kidney disease and catastrophic comorbidities (a case report).

It is evident that Acute Kidney Injury (AKI) is an independent risk factor for both the survival of patients and their kidneys. Here, we present a cas...

AI-directed formulation strategy design initiates rational drug development.

Rational drug development would be impossible without selecting the appropriate formulation route. However, pharmaceutical scientists often rely on li...

Drug toxicity prediction model based on enhanced graph neural network.

Prediction of drug toxicity remains a significant challenge and an essential process in drug discovery. Traditional machine learning algorithms strugg...

Identifying the Relative Importance of Factors Influencing Medication Compliance in General Patients Using Regularized Logistic Regression and LightGBM: Web-Based Survey Analysis.

BACKGROUND: Medication compliance, which refers to the extent to which patients correctly adhere to prescribed regimens, is influenced by various psyc...

Automated redaction of names in adverse event reports using transformer-based neural networks.

BACKGROUND: Automated recognition and redaction of personal identifiers in free text can enable organisations to share data while protecting privacy. ...

Enhancement on selenium volatilization for phytoremediation: role of plant and soil microbe interaction.

This study aimed at quantifying the potential effects of plant and soil microbial interaction on selenium (Se) volatilization, with the specific objec...

F-CPI: A Multimodal Deep Learning Approach for Predicting Compound Bioactivity Changes Induced by Fluorine Substitution.

Fluorine (F) substitution is a common method of drug discovery and development. However, there are no accurate approaches available for predicting the...

Local interpretable spammer detection model with multi-head graph channel attention network.

Fraudulent reviews posted by spammers on the online shopping websites mislead consumers' purchasing decisions. To curb fraudulent reviews, many method...

Human Collaborative Control of Lower-Limb Prosthesis Based on Game Theory and Fuzzy Approximation.

For leg prosthesis user, the soft tissue and skin under the stump of are not accustomed to weight bearing, excessive continuous contact pressure can l...