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An Explainable Transformer-Based Deep Learning Model for the Prediction of Incident Heart Failure.

Predicting the incidence of complex chronic conditions such as heart failure is challenging. Deep learning models applied to rich electronic health records may improve prediction but remain unexplainable hampering their wider use in medical practice. We aimed to develop a deep-learning framework for accurate and yet explainable prediction of 6-month incident heart failure (HF). Using 100,071 patie...

Electrocardiogram analysis of post-stroke elderly people using one-dimensional convolutional neural network model with gradient-weighted class activation mapping.

Stroke is the second leading cause of death globally after ischemic heart disease, also a risk factor of cardioembolic stroke. Thus, we postulate that heartbeats encapsulate vital signals related to stroke. With the rapid advancement of deep neural networks (DNNs), it emerges as a powerful tool to decipher intriguing heartbeat patterns associated with post-stroke patients. In this study, we propos...

Simulation platform for pattern recognition based on reservoir computing with memristor networks.

Memristive systems and devices are potentially available for implementing reservoir computing (RC) systems applied to pattern recognition. However, th...

Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning.

This study evaluates the accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning. We developed a dynamic ...

Printed synaptic transistor-based electronic skin for robots to feel and learn.

An electronic skin (e-skin) for the next generation of robots is expected to have biological skin-like multimodal sensing, signal encoding, and prepro...

Collaborative Robotic Wire + Arc Additive Manufacture and Sensor-Enabled In-Process Ultrasonic Non-Destructive Evaluation.

The demand for cost-efficient manufacturing of complex metal components has driven research for metal Additive Manufacturing (AM) such as Wire + Arc A...

Fully automatic volume segmentation using deep learning approaches to assess aneurysmal sac evolution after infrarenal endovascular aortic repair.

OBJECTIVE: Endovascular aortic repair (EVAR) surveillance relies on serial measurements of the maximal diameter despite significant inter- and intraob...

Prediction of serious outcomes based on continuous vital sign monitoring of high-risk patients.

Continuous monitoring of high-risk patients and early prediction of severe outcomes is crucial to prevent avoidable deaths. Current clinical monitorin...

Artificial Intelligence and Machine Learning for Safe Medicines.

Authors' views on the role of artificial intelligence and machine learning in pharmacovigilance. (MP4  139807 kb).

Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations.

The tools of artificial intelligence (AI) have enormous potential to enhance activities in pharmacovigilance. Pharmacovigilance experts need not be AI...

Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance.

TransCelerate reports on the results of 2019, 2020, and 2021 member company (MC) surveys on the use of intelligent automation in pharmacovigilance pro...

Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review.

INTRODUCTION: Artificial intelligence based on machine learning has made large advancements in many fields of science and medicine but its impact on p...

Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations.

Increasing availability of electronic health databases capturing real-world experiences with medical products has garnered much interest in their use ...

Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources.

With the rapid development of artificial intelligence (AI) technologies, and the large amount of pharmacovigilance-related data stored in an electroni...

Supervised Machine Learning-Based Decision Support for Signal Validation Classification.

INTRODUCTION: Signal validation in pharmacovigilance is the process of evaluating data to decide whether evidence is sufficient to justify further ass...

LEOD-Net: Learning Line-Encoded Bounding Boxes for Real-Time Object Detection.

This paper proposes a learnable line encoding technique for bounding boxes commonly used in the object detection task. A bounding box is simply encode...

iProm-Zea: A two-layer model to identify plant promoters and their types using convolutional neural network.

A promoter is a short DNA sequence near the start codon, responsible for initiating the transcription of a specific gene in the genome. The accurate r...

Identification of hand-foot syndrome from cancer patients' blog posts: BERT-based deep-learning approach to detect potential adverse drug reaction symptoms.

Early detection and management of adverse drug reactions (ADRs) is crucial for improving patients' quality of life. Hand-foot syndrome (HFS) is one of...

Using Robot-Based Variables during Upper Limb Robot-Assisted Training in Subacute Stroke Patients to Quantify Treatment Dose.

In post-stroke motor rehabilitation, treatment dose description is estimated approximately. The aim of this retrospective study was to quantify the tr...

Multimodal treatment efficacy differs in dependence of core symptom profiles in adult Attention-Deficit/Hyperactivity Disorder: An analysis of the randomized controlled COMPAS trial.

There is broad consensus that to improve the treatment of adult Attention-Deficit/Hyperactivity Disorder (ADHD), the various therapy options need to b...