prikaz prve stranice dokumenta Machine-learning-assisted donor-recipient matching for orthotopic liver transplantation
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Machine-learning-assisted donor-recipient matching for orthotopic liver transplantation
Na: Transplantation 2023;107 Suppl 9:eP-251/ - The 2023 Joint International Congress of ILTS, ELITA & LICAGE. Rotterdam, Nizozemska, 2023. https://doi.org/10.1097/01.tp.0000978836.44371.fe
Bezjak, Miran; Stresec, Ivan; Jadrijević, Stipislav; Kocman, Branislav; Filipec Kanižaj, Tajana; Antonijević, Miro; Dalbelo Bašić, Bojana; Mikulić, Danko

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Bezjak, M., Stresec, I., Jadrijević, S., Kocman, B., Filipec Kanižaj, T., Antonijević, M. ... Mikulić, D. Machine-learning-assisted donor-recipient matching for orthotopic liver transplantation [PowerPoint prezentacija]. Preuzeto s https://urn.nsk.hr/urn:nbn:hr:264:071472

Bezjak, Miran, et al. "Machine-learning-assisted donor-recipient matching for orthotopic liver transplantation." Transplantation 2023;107 Suppl 9:eP-251/ - The 2023 Joint International Congress of ILTS, ELITA & LICAGE, 03.05.2023.-06.05.2023., Rotterdam, Nizozemska. Prezentacija. https://urn.nsk.hr/urn:nbn:hr:264:071472

Bezjak, Miran, Ivan Stresec, Stipislav Jadrijević, Branislav Kocman, Tajana Filipec Kanižaj, Miro Antonijević, Bojana Dalbelo Bašić i Danko Mikulić. "Machine-learning-assisted donor-recipient matching for orthotopic liver transplantation." Prezentacija sa skupa Transplantation 2023;107 Suppl 9:eP-251/ - The 2023 Joint International Congress of ILTS, ELITA & LICAGE, Rotterdam, Nizozemska, 03.05.2023.-06.05.2023. https://urn.nsk.hr/urn:nbn:hr:264:071472

Bezjak, M., et al. 'Machine-learning-assisted donor-recipient matching for orthotopic liver transplantation' [Prezentacija], Transplantation 2023;107 Suppl 9:eP-251/ - The 2023 Joint International Congress of ILTS, ELITA & LICAGE. 03.05.2023.-06.05.2023. Preuzeto s: https://urn.nsk.hr/urn:nbn:hr:264:071472

Bezjak M, Stresec I, Jadrijević S, Kocman B, Filipec Kanižaj T, Antonijević M, i sur.. Machine-learning-assisted donor-recipient matching for orthotopic liver transplantation. [Prezentacija] [pristupljeno 14.12.2024.] Dostupno na: https://urn.nsk.hr/urn:nbn:hr:264:071472

M. Bezjak, et al., "Machine-learning-assisted donor-recipient matching for orthotopic liver transplantation", prezentirano na: Transplantation 2023;107 Suppl 9:eP-251/ - The 2023 Joint International Congress of ILTS, ELITA & LICAGE, Rotterdam, 03.05.2023.-06.05.2023. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:264:071472

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Naslov (engleski)Machine-learning-assisted donor-recipient matching for orthotopic liver transplantation
AutorMiran Bezjak
AutorIvan Stresec
AutorStipislav Jadrijević
AutorBranislav Kocman
AutorTajana Filipec Kanižaj
AutorMiro Antonijević
AutorBojana Dalbelo Bašić
AutorDanko Mikulić
Autorova ustanova(Klinička bolnica Merkur)
Znanstveno / umjetničko
područje, polje i grana		BIOMEDICINA I ZDRAVSTVO
Kliničke medicinske znanosti
Kirurgija
Sažetak (engleski)
Liver transplant allocation policies evolve over time. With no universal algorithm to predict the outcome of liver transplantation allocation, donor-recipient matching still relies heavily on the experience of the transplant team. Machine learning models, using data collected on-site could offer more reliable and relevant ranking systems in comparison to traditional prognostic scores. 27 donor and recipient parameters were collected from 656 patients who underwent liver transplantation from March 2013 through December 2018 at the Merkur Clinical Hospital in Zagreb. We designed a machine learning survival ranking system, testing several different models: the regularized Cox regression, the random survival forest, gradient-boosted trees, and the support vector machine (SVM). The models were evaluated using nested cross-validation and interpreted using Shapley additive explanations (SHAP). We compared the performance of our various ranking systems and more traditional scores, namely BAR and ET-DRI. As a measure of comparison, we employed Uno’s concordance index estimator, which was appropriate for the high censorship rates in our data. Our best ranking system, based on gradient boosted trees, achieved an average concordance index of 65% on the data, outperforming traditional scores that achieved a maximum concordance index of only 53%. Random survival forests performed similarly to the gradient-boosted trees, while the regularized Cox and SVM models showed inferior ranking capability. Interpretation of the ranking systems yielded several relevant predictors of survival: donor CRP and sodium levels, liver steatosis, cold ischemia time, and recipient age. With machine learning, we can improve existing metrics by creating an interpretable ranking system that better fits our data. Such a system could potentially provide superior assistance in donor-recipient matching and identify risk factors in the process.
Ključne riječi (engleski)
Jezikengleski
Vrsta recenzijeRecenziran - međunarodna recenzija
DOIhttps://doi.org/10.1097/01.tp.0000978836.44371.fe
URN:NBNurn:nbn:hr:264:071472
Datum izlaganja2023-05-03
SkupNaziv: Transplantation 2023;107 Suppl 9:eP-251/ - The 2023 Joint International Congress of ILTS, ELITA & LICAGE
Lokacija: Rotterdam, Nizozemska
Organizator: International Liver Transplantation Society (ILTS)
Organizator: The European Liver and Intestine Transplant Association (ELITA)
Organizator: The Liver Intensive Care Group of Europe (LICAGE)
Datum početka: 2023-05-03
Datum završetka: 2023-05-06
Vrsta izlaganja: Predavanje
URL dokumentahttps://ilts2023.abstractserver.com/program/#/details/presentations/447
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
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Datum i vrijeme pohrane2024-02-23 09:29:42