Hospital-Based Medicine

Intensivists

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

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Best practice in rheumatology new developments in ultrasound and MRI imaging of pediatric rheumatic diseases.

The evidence base for ultrasound and MRI imaging in pediatric rheumatic diseases continues to grow, ...

Comparison of different AI systems for diagnosing sepsis, septic shock, and cardiogenic shock: a retrospective study.

Sepsis, septic shock, and cardiogenic shock are life-threatening conditions associated with high mor...

Generative AI mitigates representation bias and improves model fairness through synthetic health data.

Representation bias in health data can lead to unfair decisions and compromise the generalisability ...

Probability Score for the Diagnosis of Periprosthetic Joint Infection: Development and Validation of a Practical Multi-analyte Machine Learning Model.

Background and objective The diagnosis of periprosthetic joint infection (PJI) relies on established...

MMsurv: a multimodal multi-instance multi-cancer survival prediction model integrating pathological images, clinical information, and sequencing data.

Accurate prediction of patient survival rates in cancer treatment is essential for effective therape...

A hybrid approach for binary and multi-class classification of voice disorders using a pre-trained model and ensemble classifiers.

Recent advances in artificial intelligence-based audio and speech processing have increasingly focus...

Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study.

We created and validated an open-access AI algorithm (AIc) for assessing image segmentation and pati...

Fast and interpretable mortality risk scores for critical care patients.

OBJECTIVE: Prediction of mortality in intensive care unit (ICU) patients typically relies on black b...

GraphATC: advancing multilevel and multi-label anatomical therapeutic chemical classification via atom-level graph learning.

The accurate categorization of compounds within the anatomical therapeutic chemical (ATC) system is ...

Federated transfer learning with differential privacy for multi-omics survival analysis.

Multi-omics data often suffer from the "big $p$, small $n$" problem where the dimensionality of feat...

Multi-Manifolds fusing hyperbolic graph network balanced by pareto optimization for identifying spatial domains of spatial transcriptomics.

Identifying spatial domains for spatial transcriptomics is crucial for achieving comprehensive insig...

FactVAE: a factorized variational autoencoder for single-cell multi-omics data integration analysis.

Single-cell multi-omics technologies have revolutionized the study of cell states and functions by s...

Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting.

Drug response prediction (DRP) methods tackle the complex task of associating the effectiveness of s...

DOMSCNet: a deep learning model for the classification of stomach cancer using multi-layer omics data.

The rapid advancement of next-generation sequencing (NGS) technology and the expanding availability ...

PCLSurv: a prototypical contrastive learning-based multi-omics data integration model for cancer survival prediction.

Accurate cancer survival prediction remains a critical challenge in clinical oncology, largely due t...

Benchmarking ensemble machine learning algorithms for multi-class, multi-omics data integration in clinical outcome prediction.

The complementary information found in different modalities of patient data can aid in more accurate...

Machine Learning-Based Rapid Prediction of Torsional Performance of Personalized Peripheral Artery Stent.

The complex mechanical environment of peripheral arteries makes stents with poor torsional performan...

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