Care of terminally ill / Palliative care

MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Framework

Medical imaging segmentation is a highly active area of research, with deep learning-based methods achieving state-of-the-art results in several benchmarks. However, the lack of standardized tools for training, testing, and evaluating new methods makes the comparison of methods difficult. To address this, we introduce the Medical Imaging Segmentation Toolkit (MIST), a simple, modular, and end-to...

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 understandin...

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 ...

[An overview of investments in the development of the Russian Medtech market and the prospects for the impact of digitalization in medicine on the economic performance of companies until 2030].

Today, the topic of digitalization, the introduction of innovations based on Big Data, the complexity of technologies due to the introduction of artif...

An intentional approach to managing bias in general purpose embedding models.

Advances in machine learning for health care have brought concerns about bias from the research community; specifically, the introduction, perpetuatio...

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...

Towards Interpretable End-Stage Renal Disease (ESRD) Prediction: Utilizing Administrative Claims Data with Explainable AI Techniques.

This study explores the potential of utilizing administrative claims data, combined with advanced machine learning and deep learning techniques, to pr...

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...

EASL: A Framework for Designing, Implementing, and Evaluating ML Solutions in Clinical Healthcare Settings.

We introduce the Explainable Analytical Systems Lab (EASL) framework, an end-to-end solution designed to facilitate the development, implementation, a...

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...