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Found 7 papers, 3 papers with code
Large Language Models with Retrieval-Augmented Generation for Zero-Shot Disease Phenotyping
Identifying disease phenotypes from electronic health records (EHRs) is critical for numerous secondary uses.
Active learning for medical code assignment
Machine Learning (ML) is widely used to automatically extract meaningful information from Electronic Health Records (EHR) to support operational, clinical, and financial decision-making.
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients
This has been a major issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, particularly within electronic health records (EHR) research.
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.
