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Found 11 papers, 6 papers with code
Multi-modal Masked Siamese Network Improves Chest X-Ray Representation Learning
We evaluate our approach on three publicly available chest X-ray datasets, MIMIC-CXR, CheXpert, and NIH-14, using two vision transformer (ViT) backbones, specifically ViT-Tiny and ViT-Small.
Informative Priors Improve the Reliability of Multimodal Clinical Data Classification
We use simple and scalable Gaussian mean-field variational inference to train a Bayesian neural network using the M2D2 prior.
Privacy-preserving machine learning for healthcare: open challenges and future perspectives
Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment.
Deterioration Prediction using Time-Series of Three Vital Signs and Current Clinical Features Amongst COVID-19 Patients
This is infeasible for telehealth solutions and highlights a gap in deterioration prediction models that are based on minimal data, which can be recorded at a large scale in any clinic, nursing home, or even at the patient's home.
MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images
Multi-modal fusion approaches aim to integrate information from different data sources.
Ranked #1 on
Phenotype classification
on MIMIC-CXR, MIMIC-IV
Towards dynamic multi-modal phenotyping using chest radiographs and physiological data
The healthcare domain is characterized by heterogeneous data modalities, such as imaging and physiological data.
Meta-repository of screening mammography classifiers
Artificial intelligence (AI) is showing promise in improving clinical diagnosis.
Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs
Here, we propose a multi-label generalized zero shot learning (CXR-ML-GZSL) network that can simultaneously predict multiple seen and unseen diseases in CXR images.
Clinical prediction system of complications among COVID-19 patients: a development and validation retrospective multicentre study
In test set B (225 patient encounters), the respective system achieves 0. 90 AUROC for AKI, elevated troponin, and elevated interleukin-6, and >0. 80 AUROC for most of the other complications.
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.
Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality
Machine learning models can be used for pattern recognition in medical data in order to improve patient outcomes, such as the prediction of in-hospital mortality.
