Hospital-Based Medicine

Intensivists

Latest AI and machine learning research in intensivists for healthcare professionals.

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Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity.

Decoding emotional states from human brain activity play an important role in the brain-computer int...

ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration.

Retinal image registration is of utmost importance due to its wide applications in medical practice....

A privacy-preserving platform oriented medical healthcare and its application in identifying patients with candidemia.

Federated learning (FL) has emerged as a significant method for developing machine learning models a...

CHNet: A multi-task global-local Collaborative Hybrid Network for KRAS mutation status prediction in colorectal cancer.

Accurate prediction of Kirsten rat sarcoma (KRAS) mutation status is crucial for personalized treatm...

Development and external validation of machine learning-based models to predict patients with cellulitis developing sepsis during hospitalisation.

OBJECTIVE: Cellulitis is the most common cause of skin-related hospitalisations, and the mortality o...

An auto-segmented multi-time window dual-scale neural network for brain-computer interfaces based on event-related potentials.

Event-related potentials (ERPs) are cerebral responses to cognitive processes, also referred to as c...

The weighted multi-scale connections networks for macrodispersivity estimation.

Macrodispersivity is critical for predicting solute behaviors with dispersive transport models. Conv...

Using multi-label ensemble CNN classifiers to mitigate labelling inconsistencies in patch-level Gleason grading.

This paper presents a novel approach to enhance the accuracy of patch-level Gleason grading in prost...

Multi-scale object equalization learning network for intracerebral hemorrhage region segmentation.

Segmentation and the subsequent quantitative assessment of the target object in computed tomography ...

A Deep Learning Approach to Estimate Multi-Level Mental Stress From EEG Using Serious Games.

Stress is revealed by the inability of individuals to cope with their environment, which is frequent...

Evaluating Explanations From AI Algorithms for Clinical Decision-Making: A Social Science-Based Approach.

Explainable Artificial Intelligence (XAI) techniques generate explanations for predictions from AI m...

MASA-TCN: Multi-Anchor Space-Aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition.

Emotion recognition from electroencephalogram (EEG) signals is a critical domain in biomedical resea...

Prognosis Prediction of Diffuse Large B-Cell Lymphoma in F-FDG PET Images Based on Multi-Deep-Learning Models.

Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one of the most challenging and...

Sliding Window Optimal Transport for Open World Artifact Detection in Histopathology.

Histological images are frequently impaired by local artifacts from scanner malfunctions or iatrogen...

Multi-Grained Radiology Report Generation With Sentence-Level Image-Language Contrastive Learning.

The automatic generation of accurate radiology reports is of great clinical importance and has drawn...

Prediction of in-hospital Mortality of Intensive Care Unit Patients with Acute Pancreatitis Based on an Explainable Machine Learning Algorithm.

BACKGROUND AND AIM: Acute pancreatitis (AP) is potentially fatal. Therefore, early identification of...

Machine learning for the prediction of delirium in elderly intensive care unit patients.

PURPOSE: This study aims to develop and validate a prediction model for delirium in elderly ICU pati...

Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to met...

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