Prescriptions

Live human-robot interactive public demonstrations with automatic emotion and personality prediction.

Communication with humans is a multi-faceted phenomenon where the emotions, personality and non-verbal behaviours, as well as the verbal behaviours, play a significant role, and human-robot interaction (HRI) technologies should respect this complexity to achieve efficient and seamless communication. In this paper, we describe the design and execution of five public demonstrations made with two HRI...

Drawing parallels in human-other interactions: a trans-disciplinary approach to developing human-robot interaction methodologies.

This opinion paper discusses how human-robot interaction (HRI) methodologies can be robustly developed by drawing on insights from fields outside of HRI that explore human-other interactions. The paper presents a framework that draws parallels between HRIs, and human-human, human-animal and human-object interaction literature, by considering the morphology and use of a robot to aid the development...

Imagine how to behave: the influence of imagined contact on human-robot interaction.

Imagined contact (IC), that is, mentally simulating an interaction with an outgroup member, reduces negative attitudes towards outgroup members, incre...

Feeling committed to a robot: why, what, when and how?

The paper spells out the rationale for developing means of manipulating and of measuring people's sense of commitment to robot interaction partners. A...

Social behaviour as an emergent property of embodied curiosity: a robotics perspective.

Social interaction is an extremely complex yet vital component in daily life. We present a bottom-up approach for the emergence of social behaviours f...

Artificial Intelligence and the Future of the Drug Safety Professional.

The healthcare industry, and specifically the pharmacovigilance industry, recognizes the need to support the increasing amount of data received from i...

Predicting drug synergy for precision medicine using network biology and machine learning.

Identification of effective drug combinations for patients is an expensive and time-consuming procedure, especially for experiments. To accelerate th...

Artificial Intelligence Within Pharmacovigilance: A Means to Identify Cognitive Services and the Framework for Their Validation.

INTRODUCTION: Pharmacovigilance (PV) detects, assesses, and prevents adverse events (AEs) and other drug-related problems by collecting, evaluating, a...

The Effectiveness of the Game of Dice Task in Predicting At-Risk and Problem Gambling Among Adolescents: The Contribution of the Neural Networks.

The Game of Dice Task (GDT; Brand et al. in Neuropsychology 19:267-277, 2005a; Psychiatry Res 133:91-99, 2005b) measures decision-making under objecti...

[A machine learning model on orthokeratology lens fitting based on the data of optometry examination].

To get an orthokeratology lens fitting model according to the research of the optometry examination data, which can help to improve the work efficien...

Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.

Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving...

Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models.

Timely identification of adverse drug reactions (ADRs) is highly important in the domains of public health and pharmacology. Early discovery of potent...

Towards a Holistic Approach to Studying Human-Robot Interaction in Later Life.

BACKGROUND AND OBJECTIVES: Implementation of robotics technology in eldercare depends on successful human-robot interaction (HRI). Relying on a system...

The Application of Machine Learning Techniques in Clinical Drug Therapy.

INTRODUCTION: The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and...

Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction.

BACKGROUND: The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts ...

A Drug Repurposing Method Based on Drug-Drug Interaction Networks and Using Energy Model Layouts.

Complex network representations of reported drug-drug interactions foster computational strategies that can infer pharmacological functions which, in ...

Using Drug Expression Profiles and Machine Learning Approach for Drug Repurposing.

The cost of new drug development has been increasing, and repurposing known medications for new indications serves as an important way to hasten drug ...

Computational Prediction of Drug-Target Interactions via Ensemble Learning.

Therapeutic effects of drugs are mediated via interactions between them and their intended targets. As such, prediction of drug-target interactions is...

A Drug-Target Network-Based Supervised Machine Learning Repurposing Method Allowing the Use of Multiple Heterogeneous Information Sources.

Drug-target networks have an important role in pharmaceutical innovation, drug lead discovery, and recent drug repositioning tasks. Many different in ...

Tripartite Network-Based Repurposing Method Using Deep Learning to Compute Similarities for Drug-Target Prediction.

The drug discovery process is conventionally regarded as resource intensive and complex. Therefore, research effort has been put into a process called...