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Found 8 papers, 1 papers with code
Multimodal Variational Autoencoder for Low-cost Cardiac Hemodynamics Instability Detection
In response to these limitations, we propose a novel multimodal variational autoencoder ($\text{CardioVAE}_\text{X, G}$) to integrate low-cost chest X-ray (CXR) and electrocardiogram (ECG) modalities with pre-training on a large unlabeled dataset.
MeDSLIP: Medical Dual-Stream Language-Image Pre-training for Fine-grained Alignment
Moreover, a novel vision-language Prototypical Contr-astive Learning (ProtoCL) method is adopted in MeDSLIP to enhance the alignment within the anatomical and pathological streams.
Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Data
We show that our approach is better at both generating authentic data of the minority class and remaining within the original distribution of the real data.
Prediction of Blood Lactate Values in Critically Ill Patients: A Retrospective Multi-center Cohort Study
Outcomes were defined based on increase or decrease of serum lactate levels between the groups.
Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation
We demonstrate deep ROC analysis in two case studies and provide a toolkit in Python.
Blood lactate concentration prediction in critical care patients: handling missing values
Blood lactate concentration is a strong indicator of mortality risk in critically ill patients.
Benchmarking machine learning models on multi-centre eICU critical care dataset
This is the first public benchmark on a multi-centre critical care dataset, comparing the performance of clinical gold standard with our predictive model.
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review
There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods.
