Ghassemi M - Search Results

1

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.

Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, Boulesteix AL, Camaradou JC, Celi LA, Denaxas S, Denniston AK, Glocker B, Golub RM, Harvey H, Heinze G, Hoffman MM, Kengne AP, Lam E, Lee N, Loder EW, Maier-Hein L, Mateen BA, McCradden MD, Oakden-Rayner L, Ordish J, Parnell R, Rose S, Singh K, Wynants L, Logullo P. Collins GS, et al. Among authors: ghassemi m. BMJ. 2024 Apr 16;385:e078378. doi: 10.1136/bmj-2023-078378. BMJ. 2024. PMID: 38626948 Free PMC article.

2

Leveraging a critical care database: selective serotonin reuptake inhibitor use prior to ICU admission is associated with increased hospital mortality.

Ghassemi M, Marshall J, Singh N, Stone DJ, Celi LA. Ghassemi M, et al. Chest. 2014 Apr;145(4):745-752. doi: 10.1378/chest.13-1722. Chest. 2014. PMID: 24371841 Free PMC article.

3

State of the art review: the data revolution in critical care.

Ghassemi M, Celi LA, Stone DJ. Ghassemi M, et al. Crit Care. 2015 Mar 16;19(1):118. doi: 10.1186/s13054-015-0801-4. Crit Care. 2015. PMID: 25886756 Free PMC article. Review.

4

Sodium modelling to reduce intradialytic hypotension during haemodialysis for acute kidney injury in the intensive care unit.

Lynch KE, Ghassemi F, Flythe JE, Feng M, Ghassemi M, Celi LA, Brunelli SM. Lynch KE, et al. Among authors: ghassemi f, ghassemi m. Nephrology (Carlton). 2016 Oct;21(10):870-7. doi: 10.1111/nep.12677. Nephrology (Carlton). 2016. PMID: 26590371 Free PMC article.

5

Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database.

Wu M, Ghassemi M, Feng M, Celi LA, Szolovits P, Doshi-Velez F. Wu M, et al. Among authors: ghassemi m. J Am Med Inform Assoc. 2017 May 1;24(3):488-495. doi: 10.1093/jamia/ocw138. J Am Med Inform Assoc. 2017. PMID: 27707820 Free PMC article.

6

The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data.

Celi LA, Citi L, Ghassemi M, Pollard TJ. Celi LA, et al. Among authors: ghassemi m. PLoS One. 2019 Jan 15;14(1):e0210232. doi: 10.1371/journal.pone.0210232. eCollection 2019. PLoS One. 2019. PMID: 30645625 Free PMC article. Review.

7

Challenges to the Reproducibility of Machine Learning Models in Health Care.

Beam AL, Manrai AK, Ghassemi M. Beam AL, et al. Among authors: ghassemi m. JAMA. 2020 Jan 28;323(4):305-306. doi: 10.1001/jama.2019.20866. JAMA. 2020. PMID: 31904799 Free PMC article. No abstract available.

8

A Review of Challenges and Opportunities in Machine Learning for Health.

Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R. Ghassemi M, et al. AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:191-200. eCollection 2020. AMIA Jt Summits Transl Sci Proc. 2020. PMID: 32477638 Free PMC article.

9

Ensuring machine learning for healthcare works for all.

McCoy LG, Banja JD, Ghassemi M, Celi LA. McCoy LG, et al. Among authors: ghassemi m. BMJ Health Care Inform. 2020 Nov;27(3):e100237. doi: 10.1136/bmjhci-2020-100237. BMJ Health Care Inform. 2020. PMID: 33234535 Free PMC article. No abstract available.

10

Practical guidance on artificial intelligence for health-care data.

Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R. Ghassemi M, et al. Lancet Digit Health. 2019 Aug;1(4):e157-e159. doi: 10.1016/S2589-7500(19)30084-6. Epub 2019 Aug 9. Lancet Digit Health. 2019. PMID: 33323184 Free article. No abstract available.

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