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Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach.
Chung J, Kim D, Choi J, Yune S, Song KD, Kim S, Chua M, Succi MD, Conklin J, Longo MGF, Ackman JB, Petranovic M, Lev MH, Do S. Chung J, et al. Among authors: kim d, kim s. Sci Rep. 2022 Dec 7;12(1):21164. doi: 10.1038/s41598-022-24721-5. Sci Rep. 2022. PMID: 36476724 Free PMC article.
Tackling prediction uncertainty in machine learning for healthcare.
Chua M, Kim D, Choi J, Lee NG, Deshpande V, Schwab J, Lev MH, Gonzalez RG, Gee MS, Do S. Chua M, et al. Among authors: kim d. Nat Biomed Eng. 2023 Jun;7(6):711-718. doi: 10.1038/s41551-022-00988-x. Epub 2022 Dec 29. Nat Biomed Eng. 2023. PMID: 36581695 Review.
Author Correction: Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach.
Chung J, Kim D, Choi J, Yune S, Song KD, Kim S, Chua M, Succi MD, Conklin J, Longo MGF, Ackman JB, Petranovic M, Lev MH, Do S. Chung J, et al. Among authors: kim d, kim s. Sci Rep. 2023 Mar 15;13(1):4296. doi: 10.1038/s41598-023-31333-0. Sci Rep. 2023. PMID: 36922618 Free PMC article. No abstract available.
A complete genomic screen for multiple sclerosis underscores a role for the major histocompatability complex. The Multiple Sclerosis Genetics Group.
Haines JL, Ter-Minassian M, Bazyk A, Gusella JF, Kim DJ, Terwedow H, Pericak-Vance MA, Rimmler JB, Haynes CS, Roses AD, Lee A, Shaner B, Menold M, Seboun E, Fitoussi RP, Gartioux C, Reyes C, Ribierre F, Gyapay G, Weissenbach J, Hauser SL, Goodkin DE, Lincoln R, Usuku K, Oksenberg JR, et al. Haines JL, et al. Among authors: kim dj. Nat Genet. 1996 Aug;13(4):469-71. doi: 10.1038/ng0896-469. Nat Genet. 1996. PMID: 8696344
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