Care of terminally ill / Palliative care

BGAT-CCRF: A novel end-to-end model for knowledge graph noise correction.

Knowledge graph (KG) noise correction aims to select suitable candidates to correct the noises in KGs. Most of the existing studies have limited performance in repairing the noisy triple that contains more than one incorrect entity or relation, which significantly constrains their implementation in real-world KGs. To overcome this challenge, we propose a novel end-to-end model (BGAT-CCRF) that ach...

Hematoma expansion prediction in intracerebral hemorrhage patients by using synthesized CT images in an end-to-end deep learning framework.

Spontaneous intracerebral hemorrhage (ICH) is a type of stroke less prevalent than ischemic stroke but associated with high mortality rates. Hematoma expansion (HE) is an increase in the bleeding that affects 30%-38% of hemorrhagic stroke patients. It is observed within 24 h of onset and associated with patient worsening. Clinically it is relevant to detect the patients that will develop HE from t...

A Contrastive-Learning-Based Deep Neural Network for Cancer Subtyping by Integrating Multi-Omics Data.

BACKGROUND: Accurate identification of cancer subtypes is crucial for disease prognosis evaluation and personalized patient management. Recent advance...

Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial.

Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and p...

EFNet: A multitask deep learning network for simultaneous quantification of left ventricle structure and function.

PURPOSE: The purpose of this study is to develop an automated method using deep learning for the reliable and precise quantification of left ventricle...

An end-to-end framework for the prediction of protein structure and fitness from single sequence.

Significant research progress has been made in the field of protein structure and fitness prediction. Particularly, single-sequence-based structure pr...

Advancing Point-of-Care Testing by Application of Machine Learning Techniques and Artificial Intelligence.

The promise of artificial intelligence has generated enthusiasm among patients, health care professionals, and technology developers who seek to lever...

Sexually dimorphic computational histopathological signatures prognostic of overall survival in high-grade gliomas via deep learning.

High-grade glioma (HGG) is an aggressive brain tumor. Sex is an important factor that differentially affects survival outcomes in HGG. We used an end-...

An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.

Untargeted metabolomic analysis using mass spectrometry provides comprehensive metabolic profiling, but its medical application faces challenges of co...

An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping.

Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its ...

Self-normalization for a 1 mmresolution clinical PET system using deep learning.

This work proposes, for the first time, an image-based end-to-end self-normalization framework for positron emission tomography (PET) using conditiona...

Artificial Intelligence for Radiation Treatment Planning: Bridging Gaps From Retrospective Promise to Clinical Reality.

Artificial intelligence (AI) radiation therapy (RT) planning holds promise for enhancing the consistency and efficiency of the RT planning process. De...

End-to-end reproducible AI pipelines in radiology using the cloud.

Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lack...

Actionable Predictions of Human Pharmacokinetics at the Drug Design Stage.

We present a novel computational approach for predicting human pharmacokinetics (PK) that addresses the challenges of early stage drug design. Our stu...

Assessment of left ventricular wall thickness and dimension: accuracy of a deep learning model with prediction uncertainty.

Left ventricular (LV) geometric patterns aid clinicians in the diagnosis and prognostication of various cardiomyopathies. The aim of this study is to ...

Euclidean-Distance-Preserved Feature Reduction for efficient person re-identification.

Person Re-identification (Re-ID) aims to match person images across non-overlapping cameras. The existing approaches formulate this task as fine-grain...

CDT-CAD: Context-Aware Deformable Transformers for End-to-End Chest Abnormality Detection on X-Ray Images.

Deep learning methods have achieved great success in medical image analysis domain. However, most of them suffer from slow convergency and high comput...

Exploring driving behavioral characteristics in pre-, in-, and post-conflict stages based on car-following trajectory data.

This study investigates driving behaviour in different stages of rear-end conflicts using vehicle trajectory data. Three conflict stages (pre-, in-, a...

Two-step optimization for accelerating deep image prior-based PET image reconstruction.

Deep learning, particularly convolutional neural networks (CNNs), has advanced positron emission tomography (PET) image reconstruction. However, it re...

Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans.

Measuring suicide risk fluctuation remains difficult, especially for high-suicide risk patients. Our study addressed this issue by leveraging Dynamic ...