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Found 5 papers, 1 papers with code
Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation
In recent years, machine learning models have rapidly become better at generating clinical consultation notes; yet, there is little work on how to properly evaluate the generated consultation notes to understand the impact they may have on both the clinician using them and the patient's clinical safety.
Towards more patient friendly clinical notes through language models and ontologies
Our method based on a language model trained on medical forum data generates simpler sentences while preserving both grammar and the original meaning, surpassing the current state of the art.
Towards objectively evaluating the quality of generated medical summaries
We propose a method for evaluating the quality of generated text by asking evaluators to count facts, and computing precision, recall, f-score, and accuracy from the raw counts.
Direct numerical simulations of transient turbulent jets: vortex-interface interactions
The thinning of the lobes induces the creation of holes which expand to form liquid threads that undergo capillary breakup to form droplets.
Fluid Dynamics
Can Embeddings Adequately Represent Medical Terminology? New Large-Scale Medical Term Similarity Datasets Have the Answer!
The novel datasets thus form a challenging new benchmark for the development of medical embeddings able to accurately represent the whole medical terminology.
