122 citations · 259 across the 14 of their papers we have counts for
5 papers · 1 filter
Improving the robustness and accuracy of biomedical language models through adversarial training
Milad Moradi, Matthias Samwald
Deep transformer neural network models have improved the predictive accuracy of intelligent text processing systems in the biomedical domain. They have obtained state-of-the-art pe…
GPT-3 Models are Poor Few-Shot Learners in the Biomedical Domain
Milad Moradi, Kathrin Blagec, Florian Haberl +1
Deep neural language models have set new breakthroughs in many tasks of Natural Language Processing (NLP). Recent work has shown that deep transformer language models (pretrained o…
Deep learning models are not robust against noise in clinical text
Milad Moradi, Kathrin Blagec, Matthias Samwald
Artificial Intelligence (AI) systems are attracting increasing interest in the medical domain due to their ability to learn complicated tasks that require human intelligence and ex…
Evaluating the Robustness of Neural Language Models to Input Perturbations
Milad Moradi, Matthias Samwald
High-performance neural language models have obtained state-of-the-art results on a wide range of Natural Language Processing (NLP) tasks. However, results for common benchmark dat…
Hybrid deep learning methods for phenotype prediction from clinical notes
Sahar Khalafi, Nasser Ghadiri, Milad Moradi
Identifying patient cohorts from clinical notes in secondary electronic health records is a fundamental task in clinical information management. However, with the growing number of…