{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T18:18:22Z","timestamp":1787681902185,"version":"build-2784847793"},"reference-count":23,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"1","funder":[{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["#2017\/50343-2, #2019\/01664-6, #2020\/09850-0, #2021\/11.9050, and #2025\/07114-9."],"award-info":[{"award-number":["#2017\/50343-2, #2019\/01664-6, #2020\/09850-0, #2021\/11.9050, and #2025\/07114-9."]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["spj.science.org"],"crossmark-restriction":true},"short-container-title":["Comput Struct Biotechnol J"],"published-print":{"date-parts":[[2026,3,1]]},"abstract":"<jats:p>Accurate prediction of posttransplant survival is critical for optimizing liver allocation under organ scarcity. Although the Model for End-Stage Liver Disease (MELD) is widely used for prioritization, it was not designed to estimate posttransplant outcomes, limiting its utility for individualized risk stratification. We developed and temporally validated machine-learning models to predict 12-month survival after liver transplantation using routinely collected donor\u2013recipient variables from a real-world cohort. Sequential temporal validation was used to approximate prospective deployment. Performance was evaluated in terms of discrimination (receiver operating characteristic\u2013area under the curve [ROC-AUC] and precision\u2013recall area under the curve), calibration (Brier score), and decision-analytic evaluation using decision curve analysis. Class imbalance was addressed through cost-sensitive learning, and predicted probabilities were recalibrated with Platt scaling. Across temporal validation windows, discrimination remained modest, with ROC-AUC values below 0.70 and reaching 0.599 in the final window, reflecting the complexity of posttransplant outcomes in heterogeneous clinical populations. Calibration remained relatively stable (Brier score \u2248 0.18). Within the evaluated threshold range, the machine-learning models yielded a positive net benefit and generally exceeded the transformed MELD baseline in decision-analytic comparisons. However, these findings should be interpreted cautiously given the modest discrimination and the exploratory nature of the MELD transformation used for cross-model comparison. Logistic regression provided the most consistent balance of discrimination, calibration, and net benefit. Overall, calibrated models with modest discrimination may still offer complementary risk information. However, further external validation, threshold-specific evaluation, and prospective testing are required before such models can be considered for operational decision support.<\/jats:p>","DOI":"10.34133\/csbj.0091","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T07:26:25Z","timestamp":1776324385000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning for Predicting 12-Month Survival after Liver Transplantation: Temporal Validation, Calibration, and Decision-Analytic Evaluation"],"prefix":"10.34133","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5179-3544","authenticated-orcid":false,"given":"Jose Ricardo","family":"de Oliveira","sequence":"first","affiliation":[{"name":"Artificial Intelligence and Analytics Department, \rInstitute for Technological Research, S\u00e3o Paulo 05508-901, SP, Brazil."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7114-6830","authenticated-orcid":true,"given":"Adriano Galindo","family":"Leal","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Analytics Department, \rInstitute for Technological Research, S\u00e3o Paulo 05508-901, SP, Brazil."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2131-7707","authenticated-orcid":false,"given":"Rodrigo Luiz","family":"Macacari","sequence":"additional","affiliation":[{"name":"Hospital das Clinicas (HCFMUSP), Faculdade de Medicina, \rUniversidade de S\u00e3o Paulo, S\u00e3o Paulo 05403-010, SP, Brazil."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3667-9660","authenticated-orcid":false,"given":"Ben-Hur","family":"Ferraz-Neto","sequence":"additional","affiliation":[{"name":"Instituto do Figado, \rRede Am\u00e9ricas, S\u00e3o Paulo 04534-011, SP, Brazil."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2026,6,5]]},"reference":[{"key":"e_1_3_4_2_2","unstructured":"Mukherjee S. The song of the cell. New York (NY): Scribner; 2022."},{"issue":"1","key":"e_1_3_4_3_2","article-title":"Transplante de f\u00edgado no Brasil entre 2010 e 2021: Sobrevida de 30 dias, Brazilian","volume":"26","author":"Luna Nascimento SM","year":"2023","unstructured":"de Luna Nascimento SM, Fabris MEM, Barros JM, Ribeiro LM, Frizanco AB, Santiago ALP, Hoffmann-Santos HD, Nogueira PLB. Transplante de f\u00edgado no Brasil entre 2010 e 2021: Sobrevida de 30 dias, Brazilian. J Transplant. 2023;26(1): Article e3823.","journal-title":"J Transplant"},{"issue":"1","key":"e_1_3_4_4_2","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.artmed.2013.02.004","article-title":"Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks","volume":"58","author":"Cruz-Ram\u00edrez M","year":"2013","unstructured":"Cruz-Ram\u00edrez M, Herv\u00e1s-Mart\u00ednez C, Fern\u00e1ndez JC, Brice\u00f1o J, de la Mata M. Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks. Artif Intell Med. 2013;58(1):37\u201349.","journal-title":"Artif Intell Med"},{"issue":"4","key":"e_1_3_4_5_2","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1053\/he.2000.5852","article-title":"A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts","volume":"31","author":"Malinchoc M","year":"2000","unstructured":"Malinchoc M, Kamath PS, Gordon FD, Peine CJ, Rank J, ter Borg PC. A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts. 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Liver Transpl. 2020;26(10):1224\u20131232.","journal-title":"Liver Transpl"},{"issue":"11","key":"e_1_3_4_8_2","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1111\/tri.13710","article-title":"The trials and tribulations of liver allocation","volume":"33","author":"Lee E","year":"2020","unstructured":"Lee E, Johnston CJC, Oniscu GC. The trials and tribulations of liver allocation. Transpl Int. 2020;33(11):1343\u20131352.","journal-title":"Transpl Int"},{"key":"e_1_3_4_9_2","doi-asserted-by":"publisher","DOI":"10.1002\/lt.24993"},{"key":"e_1_3_4_10_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.ebiom.2020.103064","article-title":"AI finally provides augmented intelligence to liver surgeons","volume":"61","author":"Golse N","year":"2020","unstructured":"Golse N. AI finally provides augmented intelligence to liver surgeons. EBioMedicine. 2020;61: Article 103064.","journal-title":"EBioMedicine"},{"issue":"1","key":"e_1_3_4_11_2","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1186\/s12910-023-00983-0","article-title":"Should AI allocate livers for transplant? Public attitudes and ethical considerations","volume":"24","author":"Drezga-Kleiminger M","year":"2023","unstructured":"Drezga-Kleiminger M, Demaree-Cotton J, Koplin J, Savulescu J, Wilkinson D. Should AI allocate livers for transplant? Public attitudes and ethical considerations. 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Medicina Lithuania. 2022;58(12):1743.","journal-title":"Medicina Lithuania"},{"issue":"5","key":"e_1_3_4_17_2","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0252068","article-title":"Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation","volume":"16","author":"Guijo-Rubio D","year":"2021","unstructured":"Guijo-Rubio D, Brice\u00f1o J, Guti\u00e9rrez PA, Ayll\u00f3n MD, Ciria R, Herv\u00e1s-Mart\u00ednez C. Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation. PLOS ONE. 2021;16(5): Article e0252068.","journal-title":"PLOS ONE"},{"key":"e_1_3_4_18_2","doi-asserted-by":"crossref","unstructured":"Raju J Sathyalakshmi S. Long-term survival prediction of liver transplantation using deep learning techniques. 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