Prescriptions

Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU.

Fluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. We compared the ability of traditional regression techniques and different ML-based modeling approaches to identify clinically meaningful fluid overlo...

Current Trends and Challenges in Drug-Likeness Prediction: Are They Generalizable and Interpretable?

: Drug-likeness of a compound is an overall assessment of its potential to succeed in clinical trials, and is essential for economizing research expenditures by filtering compounds with unfavorable properties and poor development potential. To this end, a robust drug-likeness prediction method is indispensable. Various approaches, including discriminative rules, statistical models, and machine lea...

A simplified similarity-based approach for drug-drug interaction prediction.

Drug-drug interactions (DDIs) are a critical component of drug safety surveillance. Laboratory studies aimed at detecting DDIs are typically difficult...

Validation of a Natural Language Machine Learning Model for Safety Literature Surveillance.

INTRODUCTION: As part of routine safety surveillance, thousands of articles of potential interest are manually triaged for review by safety surveillan...

Deep Generative Models in Drug Molecule Generation.

The discovery of new drugs has important implications for human health. Traditional methods for drug discovery rely on experiments to optimize the str...

Artificial intelligence: Machine learning approach for screening large database and drug discovery.

Recent research in drug discovery dealing with many faces difficulties, including development of new drugs during disease outbreak and drug resistance...

Deep learning-enabled natural language processing to identify directional pharmacokinetic drug-drug interactions.

BACKGROUND: During drug development, it is essential to gather information about the change of clinical exposure of a drug (object) due to the pharmac...

Effect of SARS-CoV-2 spike protein exposure on ACE2 and interleukin 6 productions in human adipocytes: An in-vitro study.

Since adipocytes play a crucial role in pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection due to their interactio...

Advanced deep learning techniques for early disease prediction in cauliflower plants.

Agriculture plays a pivotal role in the economies of developing countries by providing livelihoods, sustenance, and employment opportunities in rural ...

The preliminary in vitro study and application of deep learning algorithm in cone beam computed tomography image implant recognition.

To properly repair and maintain implants, which are bone tissue implants that replace natural tooth roots, it is crucial to accurately identify their ...

Heterogeneous context interaction network for vehicle re-identification.

Capturing global and subtle discriminative information using attention mechanisms is essential to address the challenge of inter-class high similarity...

Efficacy and Safety of Chinese Herbal Medicine in Patients with Acute Intracerebral Hemorrhage: Protocol for a Randomized Placebo-Controlled Double-Blinded Multicenter Trial.

INTRODUCTION: The popular traditional Chinese medicine (TCM) compound FYTF-919 (Zhong Feng Xing Nao prescription) may improve outcome from acute intra...

MACI: A machine learning-based approach to identify drug classes of antibiotic resistance genes from metagenomic data.

Novel methodologies are now essential for identification of antibiotic resistant pathogens in order to resist them. Here, we are presenting a model, M...

Uncovering hidden therapeutic indications through drug repurposing with graph neural networks and heterogeneous data.

Drug repurposing has gained the attention of many in the recent years. The practice of repurposing existing drugs for new therapeutic uses helps to si...

Machine learning in medication prescription: A systematic review.

BACKGROUND: Medication prescription is a complex process that could benefit from current research and development in machine learning through decision...

A practical guide to machine-learning scoring for structure-based virtual screening.

Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and prote...

Psychometric Properties of a Machine Learning-Based Patient-Reported Outcome Measure on Medication Adherence: Single-Center, Cross-Sectional, Observational Study.

BACKGROUND: Medication adherence plays a critical role in controlling the evolution of chronic disease, as low medication adherence may lead to worse ...

Quantum computing and machine learning for Arabic language sentiment classification in social media.

With the increasing amount of digital data generated by Arabic speakers, the need for effective and efficient document classification techniques is mo...

Recent Studies of Artificial Intelligence on Drug Absorption.

Absorption is an important area of research in pharmacochemistry and drug development, because the drug has to be absorbed before any drug effects can...

Predicting discrete-time bifurcations with deep learning.

Many natural and man-made systems are prone to critical transitions-abrupt and potentially devastating changes in dynamics. Deep learning classifiers ...