Critical Care

Latest AI and machine learning research in critical care for healthcare professionals.

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Comparison of Mortality Predictive Models of Sepsis Patients Based on Machine Learning.

Objective To compare the performance of five machine learning models and SAPS II score in predicting...

A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia.

Nowadays, the complexity of disease mechanisms and the inadequacy of single-target therapies in rest...

Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening.

Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of the most...

DLF-Sul: a multi-module deep learning framework for prediction of S-sulfinylation sites in proteins.

Protein S-sulfinylation is an important posttranslational modification that regulates a variety of c...

Multi-model fusion short-term power load forecasting based on improved WOA optimization.

The high accuracy of short-term power load forecasting has a pivotal role in helping power companies...

Lessons in machine learning model deployment learned from sepsis.

In three recent and related publications, researchers from Johns Hopkins University and Bayesian Hea...

Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks.

Automatic arrhythmia detection is very important for cardiovascular health. It is generally performe...

[ST segment morphological classification based on support vector machine multi feature fusion].

ST segment morphology is closely related to cardiovascular disease. It is used not only for characte...

Multi-attribute decision-making method with triangular fuzzy numbers based on regret theory and the catastrophe progression method.

The purpose of this paper was to develop a novel triangular fuzzy method for multi-attribute decisio...

Development and validation of a deep learning model to predict the survival of patients in ICU.

BACKGROUND: Patients in the intensive care unit (ICU) are often in critical condition and have a hig...

Glucose trajectory prediction by deep learning for personal home care of type 2 diabetes mellitus: modelling and applying.

Glucose management for people with type 2 diabetes mellitus is essential but challenging due to the ...

Chinese medical dialogue information extraction via contrastive multi-utterance inference.

Medical Dialogue Information Extraction (MDIE) is a promising task for modern medical care systems, ...

Hyperspectral image super-resolution based on the transfer of both spectra and multi-level features.

Existing hyperspectral image (HSI) super-resolution methods fusing a high-resolution RGB image (HR-R...

DEMO2: Assemble multi-domain protein structures by coupling analogous template alignments with deep-learning inter-domain restraint prediction.

Most proteins in nature contain multiple folding units (or domains). The revolutionary success of Al...

Testing robot-based assist-as-needed therapy for improving active participation of a patient during early neurorehabilitation: a case study.

In this study, a patient in the Intensive Care-Unit received robot-based mobilization therapy with a...

Multi-Expert Deep Networks for Multi-Disease Detection in Retinal Fundus Images.

Automatic diagnosis of eye diseases from retinal fundus images is quite challenging. Common public d...

An Ensemble of Deep Learning Frameworks for Predicting Respiratory Anomalies.

This paper evaluates a range of deep learning frameworks for detecting respiratory anomalies from in...

CapNet: A Deep Learning-based Framework for Estimation of Capnograph Signal from PPG.

Ambulatory respiration signal extraction system is required to maintain continuous surveillance of a...

MSGAN: Multi-Stage Generative Adversarial Networks for Cross-Modality Domain Adaptation.

Domain adaptation has become an important topic because the trained neural networks from the source ...

MVD-Net: Semantic Segmentation of Cataract Surgery Using Multi-View Learning.

Semantic segmentation of surgery scenarios is a fundamental task for computer-aided surgery systems....

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