Prescriptions

Geometric Molecular Graph Representation Learning Model for Drug-Drug Interactions Prediction.

Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Therefore, it is important to predict potential drug interactions since it can provide combination strategies of drugs for systematic and effective treatment. Existing deep learning-based methods often rely on DDI functional networks, or use them as an important part ...

Nerve-Inspired Optical Waveguide Stretchable Sensor Fusing Wireless Transmission and AI Enabling Smart Tele-Healthcare.

Flexible strain monitoring of hand and joint muscle movement is recognized as an effective method for the diagnosis and rehabilitation of neurological diseases such as stroke and Parkinson's disease. However, balancing high sensitivity and large strain, improving wearing comfort, and solving the separation of diagnosis and treatment are important challenges for further building tele-healthcare sys...

AI-based medication adherence prediction in patients with schizophrenia and attenuated psychotic disorders.

OBJECTIVE: The capacity of machine-learning algorithms to predict medication adherence was assessed using data from AiCure, a computer vision-assisted...

MIRD Pamphlet No. 31: MIRDcell V4-Artificial Intelligence Tools to Formulate Optimized Radiopharmaceutical Cocktails for Therapy.

Radiopharmaceutical cocktails have been developed over the years to treat cancer. Cocktails of agents are attractive because 1 radiopharmaceutical is ...

On the role of visual feedback and physiotherapist-patient interaction in robot-assisted gait training: an eye-tracking and HD-EEG study.

BACKGROUND: Treadmill based Robotic-Assisted Gait Training (t-RAGT) provides for automated locomotor training to help the patient achieve a physiologi...

Prediction of Medication-Related Osteonecrosis of the Jaw in Patients Receiving Antiresorptive Therapy Using Machine Learning Models.

BACKGROUND: Medication-related osteonecrosis of the jaw (MRONJ) is a serious complication associated with the use of antiresorptive agents, impacting ...

Interpretable prediction of drug-drug interactions via text embedding in biomedical literature.

Polypharmacy is a promising approach for treating diseases, especially those with complex symptoms. However, it can lead to unexpected drug-drug inter...

Needle in a haystack: Harnessing AI in drug patent searches and prediction.

The classification codes granted by patent offices are useful instruments for simplifying the bewildering variety of patents in existence. They are si...

Predicting the physiological effects of multiple drugs using electronic health record.

Various computational models have been developed to understand the physiological effects of drug-drug interactions, which can contribute to more effec...

Application of machine learning to identify risk factors for outpatient opioid prescriptions following spine surgery.

INTRODUCTION: Spine surgery is a common source of narcotic prescriptions and carries potential for long-term opioid dependence. As prescription opioid...

Data - Knowledge driven machine learning model for cancer pain medication decisions.

BACKGROUND: Cancer pain is one of the most common symptoms in cancer patients, and drug decision-making in cancer pain management remains challenges. ...

Ultradense Electrochemical Chip and Machine Learning for High-Throughput, Accurate Anticancer Drug Screening.

Despite the potentialities of electrochemical sensors, these devices still encounter challenges in devising high-throughput and accurate drug suscepti...

Face mask identification with enhanced cuckoo optimization and deep learning-based faster regional neural network.

A mask identification and social distance monitoring system using Unmanned Aerial Vehicles (UAV) in the outdoors has been proposed for a health establ...

Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes arising during coevolutionary dynamics.

The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bact...

CMINNs: Compartment model informed neural networks - Unlocking drug dynamics.

In the field of pharmacokinetics and pharmacodynamics (PKPD) modeling, which plays a pivotal role in the drug development process, traditional models ...

Improving the closing sequences of interaction between human and robot through conversation analysis.

This study employs Conversation Analysis to create a recursive model that improves the quality of human-robot interaction. Our research goal is to cre...

Precision Opioid Prescription in ICU Surgery: Insights from an Interpretable Deep Learning Framework.

PURPOSE: Appropriate opioid management is crucial to reduce opioid overdose risk for ICU surgical patients, which can lead to severe complications. Ac...

Advancing healthcare through mobile collaboration: a survey of intelligent nursing robots research.

Mobile collaborative intelligent nursing robots have gained significant attention in the healthcare sector as an innovative solution to address the ch...

Interaction effect between data discretization and data resampling for class-imbalanced medical datasets.

BackgroundData discretization is an important preprocessing step in data mining for the transfer of continuous feature values to discrete ones, which ...

Antihypertensive Drug Recommendations for Reducing Arterial Stiffness in Patients With Hypertension: Machine Learning-Based Multicohort (RIGIPREV) Study.

BACKGROUND: High systolic blood pressure is one of the leading global risk factors for mortality, contributing significantly to cardiovascular disease...