Hospital-Based Medicine

Intensivists

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

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Showing 1135-1155 of 6,152 articles
Antioxidant, enzymes inhibitory, physicochemical and sensory properties of instant bio-yoghurts containing multi-purpose natural additives.

This study aimed to assess the antioxidant, enzyme inhibitory, physicochemical and sensory propertie...

Multi-model genome-wide association studies for appearance quality in rice.

Improving the quality of the appearance of rice is critical to meet market acceptance. Mining putati...

Measuring Implicit Bias in ICU Notes Using Word-Embedding Neural Network Models.

BACKGROUND: Language in nonmedical data sets is known to transmit human-like biases when used in nat...

Contrastive and adversarial regularized multi-level representation learning for incomplete multi-view clustering.

Incomplete multi-view clustering is a significant task in machine learning, given that complex syste...

[Potential of AI for the Treatment of Acute Respiratory Distress Syndrome (ARDS)].

Acute respiratory distress syndrome (ARDS) is still associated with high mortality rates and poses a...

EMAT: Efficient feature fusion network for visual tracking via optimized multi-head attention.

The tracking methods based on Transformer have shown great potential in visual tracking and achieved...

DeBERTa-BiLSTM: A multi-label classification model of Arabic medical questions using pre-trained models and deep learning.

It is wise to investigate past and present epidemics in the hopes of profiting from them and being b...

The clinical course of hospitalized COVID-19 patients and aggravation risk prediction models: a retrospective, multi-center Korean cohort study.

BACKGROUND: Understanding the clinical course and pivotal time points of COVID-19 aggravation is cri...

Heart rate complexity helps mortality prediction in the intensive care unit: A pilot study using artificial intelligence.

BACKGROUND: In intensive care units (ICUs), accurate mortality prediction is crucial for effective p...

Deep learning-based prediction of in-hospital mortality for sepsis.

As a serious blood infection disease, sepsis is characterized by a high mortality risk and many comp...

Integrated analysis of single-cell RNA-seq and chipset data unravels PANoptosis-related genes in sepsis.

BACKGROUND: The poor prognosis of sepsis warrants the investigation of biomarkers for predicting the...

Multi-pose-based convolutional neural network model for diagnosis of patients with central lumbar spinal stenosis.

Although the role of plain radiographs in diagnosing lumbar spinal stenosis (LSS) has declined in im...

GATR-3, a Peptide That Eradicates Preformed Biofilms of Multidrug-Resistant .

is a gram-negative bacterium that causes hospital-acquired and opportunistic infections, resulting ...

Coastal Flood risk assessment using ensemble multi-criteria decision-making with machine learning approaches.

Coastal areas are at a higher risk of flooding, and novel changes in the climate are induced to rais...

TRENDS IN CHOLESTEROL AND LIPOPROTEINS ARE ASSOCIATED WITH ACUTE RESPIRATORY DISTRESS SYNDROME INCIDENCE AND DEATH AMONG SEPSIS PATIENTS.

Objective: Compare changes in cholesterol and lipoprotein levels occurring in septic patients with a...

Regulatory effects of mangiferin on LPS-induced inflammatory responses and intestinal flora imbalance during sepsis.

Studies suggest that mangiferin (MAF) has good therapeutic effects on chronic bronchitis and hepatit...

Multi-Objective Optimization of a Long-Stroke Moving-Iron Proportional Solenoid Actuator.

In this study, the performance of a long-stroke moving-iron proportional solenoid actuator (MPSA) wa...

Multi-silicone bilateral soft physical twin as an alternative to traditional user interfaces for remote palpation: a comparative study.

Teleoperated medical technologies are a fundamental part of the healthcare system. From telemedicine...

An adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals.

Clinical risk prediction with electronic health records (EHR) using machine learning has attracted l...

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