Hospital-Based Medicine

Intensivists

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

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Showing 1072-1092 of 6,152 articles
A machine learning approach for diagnostic and prognostic predictions, key risk factors and interactions.

Machine learning (ML) has the potential to revolutionize healthcare, allowing healthcare providers t...

Determining steady-state trough range in vancomycin drug dosing using machine learning.

BACKGROUND: Vancomycin is a renally eliminated, nephrotoxic, glycopeptide antibiotic with a narrow t...

An automated ICU agitation monitoring system for video streaming using deep learning classification.

OBJECTIVE: To address the challenge of assessing sedation status in critically ill patients in the i...

Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study.

BACKGROUND: Early and reliable identification of patients with sepsis who are at high risk of mortal...

Intelligent Integrated System for Fruit Detection Using Multi-UAV Imaging and Deep Learning.

In the context of Industry 4.0, one of the most significant challenges is enhancing efficiency in se...

Multicentric development and validation of a multi-scale and multi-task deep learning model for comprehensive lower extremity alignment analysis.

Osteoarthritis of the knee, a widespread cause of knee disability, is commonly treated in orthopedic...

A deep learning method for multi-task intelligent detection of oral cancer based on optical fiber Raman spectroscopy.

In the fight against oral cancer, innovative methods like Raman spectroscopy and deep learning have ...

CRISPR-M: Predicting sgRNA off-target effect using a multi-view deep learning network.

Using the CRISPR-Cas9 system to perform base substitutions at the target site is a typical technique...

RETRACTED: Refining molecular subtypes and risk stratification of ovarian cancer through multi-omics consensus portfolio and machine learning.

Ovarian cancer (OC), known for its pronounced heterogeneity, has long evaded a unified classificatio...

MIS-Net: A deep learning-based multi-class segmentation model for CT images.

The accuracy of traditional CT image segmentation algorithms is hindered by issues such as low contr...

Time series classification of multi-channel nerve cuff recordings using deep learning.

Neurostimulation and neural recording are crucial to develop neuroprostheses that can restore functi...

Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm.

It is important to determine the risk for admission to the intensive care unit (ICU) in patients wit...

A hybrid modeling framework for generalizable and interpretable predictions of ICU mortality across multiple hospitals.

The development of reliable mortality risk stratification models is an active research area in compu...

PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding.

Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-base...

MMDB: Multimodal dual-branch model for multi-functional bioactive peptide prediction.

Bioactive peptides can hinder oxidative processes and microbial spoilage in foodstuffs and play impo...

Cm-siRPred: Predicting chemically modified siRNA efficiency based on multi-view learning strategy.

The rational modification of siRNA molecules is crucial for ensuring their drug-like properties. Mac...

Q-learning and fuzzy logic multi-tier multi-access edge clustering for 5g v2x communication.

The 5th generation (5 G) network is required to meet the growing demand for fast data speeds and the...

Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images.

BACKGROUND AND OBJECTIVE: Heart failure (HF) is a multi-faceted and life-threatening syndrome that a...

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