Care of terminally ill / Palliative care

Machine Learning-Based Prediction Models of Mortality for Intensive Care Unit Patients Using Nursing Records.

This study aimed to develop ICU mortality prediction models using a conceptual framework, focusing on nurses' concerns reflected in nursing records from the MIMIC IV database. We included 46,693 first-time ICU admissions of adults over 18 years with a minimum 24-hour stay, excluding those receiving hospice or palliative care. Predictors included demographics, clinical characteristics, and nursing ...

Towards discovery: an end-to-end system for uncovering novel biomedical relations.

Biomedical relation extraction is an ongoing challenge within the natural language processing community. Its application is important for understanding scientific biomedical literature, with many use cases, such as drug discovery, precision medicine, disease diagnosis, treatment optimization and biomedical knowledge graph construction. Therefore, the development of a tool capable of effectively ad...

Standard compliant video coding using low complexity, switchable neural wrappers

The proliferation of high resolution videos posts great storage and bandwidth pressure on cloud video services, driving the development of next-gene...

Deep STI: Deep Stochastic Time-series Imputation on Electronic Health Records.

Electronic Health Records (EHRs) are a cornerstone of modern healthcare analytics, offering rich datasets for various disease analyses through advance...

ACE: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling

Generative retrieval, which has demonstrated effectiveness in text-to-text retrieval, utilizes a sequence-to-sequence model to directly generate can...

XBG: End-to-end Imitation Learning for Autonomous Behaviour in Human-Robot Interaction and Collaboration

This paper presents XBG (eXteroceptive Behaviour Generation), a multimodal end-to-end Imitation Learning (IL) system for a whole-body autonomous hum...

ClinicalLab: Aligning Agents for Multi-Departmental Clinical Diagnostics in the Real World

LLMs have achieved significant performance progress in various NLP applications. However, LLMs still struggle to meet the strict requirements for ac...

End-to-End Hybrid Refractive-Diffractive Lens Design with Differentiable Ray-Wave Model

Hybrid refractive-diffractive lenses combine the light efficiency of refractive lenses with the information encoding power of diffractive optical el...

Advancing Supervised Local Learning Beyond Classification with Long-term Feature Bank

Local learning offers an alternative to traditional end-to-end back-propagation in deep neural networks, significantly reducing GPU memory usage. Wh...

Arrhythmic Mitral Valve Prolapse Phenotype: An Unsupervised Machine Learning Analysis Using a Multicenter Cardiac MRI Registry.

Purpose To use unsupervised machine learning to identify phenotypic clusters with increased risk of arrhythmic mitral valve prolapse (MVP). Materials ...

Ultrafast Brain MRI with Deep Learning Reconstruction for Suspected Acute Ischemic Stroke.

Background Deep learning (DL)-accelerated MRI can substantially reduce examination times. However, studies prospectively evaluating the diagnostic per...

A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac MRI Registry of Patients with Single Ventricle Physiology.

Purpose To develop an end-to-end deep learning (DL) pipeline for automated ventricular segmentation of cardiac MRI data from a multicenter registry of...

Individualized Three-Dimensional Gait Pattern Generator for Lower Limbs Rehabilitation Robots.

In the field of robotic gait rehabilitation, controlling robotic devices to follow specific human-like trajectories is often required. In recent years...

ZetaDesign: an end-to-end deep learning method for protein sequence design and side-chain packing.

Computational protein design has been demonstrated to be the most powerful tool in the last few years among protein designing and repacking tasks. In ...

Upper-Limb Rehabilitation of Patients with Neuromotor Deficits Using Impedance-Based Control of a 6-DOF Robot.

This paper presents a study on the use of impedance-based control of a 6-degree-of-freedom robot for upper-limb rehabilitation of patients with neurom...

Assessing Mode-Switching Strategies for Assistive Robotic Manipulators Using a Preliminary Version of the Novel Non-invasive Tongue-Computer Interface.

The inductive tongue-computer interface allows individuals with tetraplegia to control assistive devices. However, controlling assistive robotic arms ...

A Deep-Learning Enabled Automatic Fetal Thalamus Diameter Measurement Algorithm.

The analysis of maternal factors that impact the normal development of the fetal thalamus is an emerging field of research and requires the retrospect...

COmic: convolutional kernel networks for interpretable end-to-end learning on (multi-)omics data.

MOTIVATION: The size of available omics datasets is steadily increasing with technological advancement in recent years. While this increase in sample ...

End-to-end interpretable disease-gene association prediction.

Identifying disease-gene associations is a fundamental and critical biomedical task towards understanding molecular mechanisms, the diagnosis and trea...

Yield prediction through integration of genetic, environment, and management data through deep learning.

Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a k...