no code implementations • ACL 2022 • Francesco Moramarco, Alex Papadopoulos Korfiatis, Mark Perera, Damir Juric, Jack Flann, Ehud Reiter, Anya Belz, Aleksandar Savkov
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.
no code implementations • 23 Dec 2021 • Francesco Moramarco, Damir Juric, Aleksandar Savkov, Jack Flann, Maria Lehl, Kristian Boda, Tessa Grafen, Vitalii Zhelezniak, Sunir Gohil, Alex Papadopoulos Korfiatis, Nils Hammerla
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.
no code implementations • EACL (HumEval) 2021 • Francesco Moramarco, Damir Juric, Aleksandar Savkov, Ehud Reiter
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.
no code implementations • 3 Dec 2020 • Cristian R. Constante-Amores, Lyes Kahouadji, Assen Batchvarov, Seungwon Shin, Jalel Chergui, Damir Juric, Omar K. Matar
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
1 code implementation • 24 Mar 2020 • Claudia Schulz, Damir Juric
The novel datasets thus form a challenging new benchmark for the development of medical embeddings able to accurately represent the whole medical terminology.