Hospital-Based Medicine

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

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Pathological voice classification based on multi-domain features and deep hierarchical extreme learning machine.

The intelligent data-driven screening of pathological voice signals is a non-invasive and real-time ...

A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways.

The integrative method approaches are continuously evolving to provide accurate insights from the da...

Resonance prediction and inverse design of multi-core selective couplers based on neural networks.

Resonance analysis and structural optimization of multi-channel selective fiber couplers currently r...

Single-model multi-tasks deep learning network for recognition and quantitation of surface-enhanced Raman spectroscopy.

Surface-enhanced Raman scattering (SERS) spectroscopy analysis has long been the central task of nan...

learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data.

We introduce the R-package learnMET, developed as a flexible framework to enable a collection of ana...

Clinical utility of automatic phenotype annotation in unstructured clinical notes: intensive care unit use.

OBJECTIVE: Clinical notes contain information that has not been documented elsewhere, including resp...

Multi-omic integration by machine learning (MIMaL).

MOTIVATION: Cells respond to environments by regulating gene expression to exploit resources optimal...

dsMTL: a computational framework for privacy-preserving, distributed multi-task machine learning.

MOTIVATION: In multi-cohort machine learning studies, it is critical to differentiate between effect...

[Cascaded multi-level medical image registration method based on transformer].

In deep learning-based image registration, the deformable region with complex anatomical structures ...

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...

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