Prescriptions

Multimodal Warnings Design for In-Vehicle Robots under Driving Safety Scenarios.

In case of dangerous driving, the in-vehicle robot can provide multimodal warnings to help the driver correct the wrong operation, so the impact of the warning signal itself on driving safety needs to be reduced. This study investigates the design of multimodal warnings for in-vehicle robots under driving safety warning scenarios. Based on transparency theory, this study addressed the content and ...

A deep learning platform to assess drug proarrhythmia risk.

Drug safety initiatives have endorsed human iPSC-derived cardiomyocytes (hiPSC-CMs) as an in vitro model for predicting drug-induced cardiac arrhythmia. However, the extent to which human-defined features of in vitro arrhythmia predict actual clinical risk has been much debated. Here, we trained a convolutional neural network classifier (CNN) to learn features of in vitro action potential recordin...

LiSA: an assisted literature search pipeline for detecting serious adverse drug events with deep learning.

INTRODUCTION: Detecting safety signals attributed to a drug in scientific literature is a fundamental issue in pharmacovigilance. The constant increas...

MR-KPA: medication recommendation by combining knowledge-enhanced pre-training with a deep adversarial network.

BACKGROUND: Medication recommendation based on electronic medical record (EMR) is a research hot spot in smart healthcare. For developing computationa...

A systematic review on the state-of-the-art strategies for protein representation.

The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for r...

Improved compound-protein interaction site and binding affinity prediction using self-supervised protein embeddings.

BACKGROUND: Compound-protein interaction site and binding affinity predictions are crucial for drug discovery and drug design. In recent years, many d...

Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer perceptron approach.

We apply a heterogeneous graph convolution network (GCN) combined with a multi-layer perceptron (MLP) denoted by GCNMLP to explore the potential side ...

Editorial for "Automated MR Image Prescription of the Liver Using Deep Learning: Development, Evaluation, and Prospective Implementation".

Graph Attention Interaction Aggregation Network for Click-Through Rate Prediction.

Click-through rate prediction is a critical task for computational advertising and recommendation systems, where the key challenge is to model feature...

Scratch-AID, a deep learning-based system for automatic detection of mouse scratching behavior with high accuracy.

Mice are the most commonly used model animals for itch research and for development of anti-itch drugs. Most laboratories manually quantify mouse scra...

A Mutual Attention Model for Drug Target Binding Affinity Prediction.

Vrious machine learning approaches have been developed for drug-target interaction (DTI) prediction. One class of these approaches, DTBA, is intereste...

Semantic-Powered Explainable Model-Free Few-Shot Learning Scheme of Diagnosing COVID-19 on Chest X-Ray.

Chest X-ray (CXR) is commonly performed as an initial investigation in COVID-19, whose fast and accurate diagnosis is critical. Recently, deep learnin...

Development of a Visual Perception System on a Dual-Arm Mobile Robot for Human-Robot Interaction.

This paper presents the development of a visual-perception system on a dual-arm mobile robot for human-robot interaction. This visual system integrate...

Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review.

Ballistocardiography (BCG) and seismocardiography (SCG) are non-invasive techniques used to record the micromovements induced by cardiovascular activi...

Human monkeypox diagnose (HMD) strategy based on data mining and artificial intelligence techniques.

In May 2022, monkeypox re-emerged as a rare zoonotic disease that is an important viral disease for public health. Monkeypox can be transmitted from a...

Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions.

The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an inciden...

Toward Region-Aware Attention Learning for Scene Graph Generation.

Scene graph generation (SGGen) is a challenging task due to a complex visual context of an image. Intuitively, the human visual system can volitionall...

Emulate randomized clinical trials using heterogeneous treatment effect estimation for personalized treatments: Methodology review and benchmark.

Big data and (deep) machine learning have been ambitious tools in digital medicine, but these tools focus mainly on association. Intervention in medic...

A Proactive Attack Detection for Heating, Ventilation, and Air Conditioning (HVAC) System Using Explainable Extreme Gradient Boosting Model (XGBoost).

The advent of Industry 4.0 has revolutionized the life enormously. There is a growing trend towards the Internet of Things (IoT), which has made life ...

What place for intelligent automation and artificial intelligence to preserve and strengthen vigilance expertise in the face of increasing declarations?

In 2018, the "Ateliers de Giens" (Giens Workshops) devoted a workshop to artificial intelligence (AI) and led its experts to confirm the potential con...