UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus

Autor: Michalopoulos, George, Wang, Yuanxin, Kaka, Hussam, Chen, Helen, Wong, Alexander

Contextual word embedding models, such as BioBERT and Bio_ClinicalBERT, have achieved state-of-the-art results in biomedical natural language processing tasks by focusing their pre-training process on domain-specific corpora. However, such models do

Externí odkaz: http://arxiv.org/abs/2010.10391

netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks [version 2; peer review: 2 approved]

Autor: Pai, Shraddha, Weber, Philipp, Isserlin, Ruth, Kaka, Hussam, Hui, Shirley, Shah, Muhammad Ahmad, Giudice, Luca, Giugno, Rosalba, Nøhr, Anne Krogh, Baumbach, Jan, Bader, Gary D.

Publikováno v: F1000Research, Vol 9 (2021)
Pai, S, Weber, P, Isserlin, R, Kaka, H, Hui, S, Shah, M A, Giudice, L, Giugno, R, Nøhr, A K, Baumbach, J & Bader, G D 2021, ' netDx : Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks [version 2; peer review: 2 approved] ', F1000Research, vol. 9, 1239 . https://doi.org/10.12688/f1000research.26429.2

Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::84319ccc78e903a28ffab0ab958e91c4
https://f1000research.com/articles/9-1239/v2

Pretrained Neural Networks Accurately Identify Cancer Recurrence in Medical Record.

Autor: Kaka, Hussam, Michalopoulos, George, Subendran, Sujan, Decker, Kathleen, Lambert, Pascal, Pitz, Marshall, Singh, Harminder, Chen, Helen

Publikováno v: Studies in Health Technology & Informatics; 2022, Vol. 294, p93-97, 5p, 3 Charts

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