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Found 5 papers, 3 papers with code
Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale
With these results, we demonstrate that ConvAE can generate patient representations that lead to clinically meaningful insights.
Scaling structural learning with NO-BEARS to infer causal transcriptome networks
Constructing gene regulatory networks is a critical step in revealing disease mechanisms from transcriptomic data.
Enhancing high-content imaging for studying microtubule networks at large-scale
Such requirements are particularly challenging for high-throughput imaging, where researchers must make decisions related to the trade-off between imaging quality and speed.
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.
Deep Learning Predicts Hip Fracture using Confounding Patient and Healthcare Variables
In this study, we trained deep learning models on 17, 587 radiographs to classify fracture, five patient traits, and 14 hospital process variables.
