Prescriptions

Development of Machine Learning Models to Validate a Medication Regimen Complexity Scoring Tool for Critically Ill Patients.

INTRODUCTION: The Medication Regimen Complexity -Intensive Care Unit (MRC-ICU) is the first tool for measuring medication regimen complexity in critically ill patients. This study tested machine learning (ML) models to investigate the relationship between medication regimen complexity and patient outcomes.

Exploiting complex medical data with interpretable deep learning for adverse drug event prediction.

A variety of deep learning architectures have been developed for the goal of predictive modelling and knowledge extraction from medical records. Several models have placed strong emphasis on temporal attention mechanisms and decay factors as a means to include highly temporally relevant information regarding the recency of medical event occurrence while facilitating medical code-level interpretabi...

Predicting the frequencies of drug side effects.

A central issue in drug risk-benefit assessment is identifying frequencies of side effects in humans. Currently, frequencies are experimentally determ...

A generic physics-informed neural network-based constitutive model for soft biological tissues.

Constitutive modeling is a cornerstone for stress analysis of mechanical behaviors of biological soft tissues. Recently, it has been shown that machin...

Fluence Map Prediction Using Deep Learning Models - Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy.

Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a difficult and time-consuming task. In this study, we aim to develop a...

Ontological approach to the knowledge systematization of a toxic process and toxic course representation framework for early drug risk management.

Various types of drug toxicity can halt the development of a drug. Because drugs are xenobiotics, they inherently have the potential to cause injury. ...

A Framework for Human-Robot-Human Physical Interaction Based on N-Player Game Theory.

In order to analyze the complex interactive behaviors between the robot and two humans, this paper presents an adaptive optimal control framework for ...

Which Visual Modality Is Important When Judging the Naturalness of the Agent (Artificial Versus Human Intelligence) Providing Recommendations in the Symbolic Consumption Context?

This study aimed to explore how the type and visual modality of a recommendation agent's identity affect male university students' (1) self-reported r...

Multi-Responsive Bottlebrush-Like Unimolecules Self-Assembled Nano-Riceball for Synergistic Sono-Chemotherapy.

Improved drug loading content, bioavailability, and controlled release in targeted tissue have been major bottlenecks in the design of precision nanom...

Adverse drug event detection using reason assignments in FDA drug labels.

Adverse drug events (ADEs) are unintended incidents that involve the taking of a medication. ADEs pose significant health and financial problems world...

Quantifying drug tissue biodistribution by integrating high content screening with deep-learning analysis.

Quantitatively determining in vivo achievable drug concentrations in targeted organs of animal models and subsequent target engagement confirmation is...

Pathway-Guided Deep Neural Network toward Interpretable and Predictive Modeling of Drug Sensitivity.

To efficiently save cost and reduce risk in drug research and development, there is a pressing demand to develop methods to predict drug sensitivity ...

Multi-level feature aggregation network for instrument identification of endoscopic images.

Identification of surgical instruments is crucial in understanding surgical scenarios and providing an assistive process in endoscopic image-guided su...

Precision psychiatry in clinical practice.

The treatment of depression represents a major challenge for healthcare systems and choosing among the many available drugs without objective guidance...

Using social robot PLEO to enhance the well-being of hospitalised children.

Hospitalisation is stressful for children. Play material is often offered for distraction and comfort. We explored how contact with social robot PLEO ...

A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes.

Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds ...

Expectations vs. actual behavior of a social robot: An experimental investigation of the effects of a social robot's interaction skill level and its expected future role on people's evaluations.

Since social robots are increasingly entering areas of people's personal lives, it is crucial to examine what affects people's perceptions and evaluat...

Optimising anthocyanin extraction from strawberry fruits using response surface methodology and application in yoghurt as natural colorants and antioxidants.

The aim of the present study was to optimise the extraction conditions of anthocyanins from strawberry fruits and incorporate them in yoghurt to achie...

Comparing deep learning architectures for sentiment analysis on drug reviews.

Since the turn of the century, as millions of user's opinions are available on the web, sentiment analysis has become one of the most fruitful researc...

Evolution and impact of bias in human and machine learning algorithm interaction.

Traditionally, machine learning algorithms relied on reliable labels from experts to build predictions. More recently however, algorithms have been re...