Machine learning: it takes more than select models to draw general conclusions

Affiliations

¹ Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen, Groningen, the Netherlands.
² Division of Clinical Chemistry, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden; Clinical Chemistry, Karolinska University Laboratory, Karolinska University Hospital, Stockholm, Sweden.
³ Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen, Groningen, the Netherlands. Electronic address: m.h.de.borst@umcg.nl.

Comment

Tamas Szili-Torok et al. Kidney Int. 2023 Nov.

. 2023 Nov;104(5):1035-1036.

doi: 10.1016/j.kint.2023.07.018.

¹ Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen, Groningen, the Netherlands.
² Division of Clinical Chemistry, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden; Clinical Chemistry, Karolinska University Laboratory, Karolinska University Hospital, Stockholm, Sweden.
³ Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen, Groningen, the Netherlands. Electronic address: m.h.de.borst@umcg.nl.

No abstract available

Comment in

The authors reply.
Truchot A, Raynaud M, Loupy A. Truchot A, et al. Kidney Int. 2023 Nov;104(5):1036. doi: 10.1016/j.kint.2023.07.017. Kidney Int. 2023. PMID: 37863625 No abstract available.

Machine learning does not outperform traditional statistical modelling for kidney allograft failure prediction.
Truchot A, Raynaud M, Kamar N, Naesens M, Legendre C, Delahousse M, Thaunat O, Buchler M, Crespo M, Linhares K, Orandi BJ, Akalin E, Pujol GS, Silva HT Jr, Gupta G, Segev DL, Jouven X, Bentall AJ, Stegall MD, Lefaucheur C, Aubert O, Loupy A. Truchot A, et al. Kidney Int. 2023 May;103(5):936-948. doi: 10.1016/j.kint.2022.12.011. Epub 2022 Dec 23. Kidney Int. 2023. PMID: 36572246