{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T23:24:51Z","timestamp":1787009091000,"version":"build-2736575974"},"reference-count":36,"publisher":"Elsevier BV","issue":"4","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100002491","name":"Bristol-Myers Squibb Company","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100002491","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016912","name":"MSD France SAS","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016912","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001665","name":"French National Research Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006483","name":"AbbVie Inc","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006483","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005564","name":"Gilead Sciences Inc","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005564","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004337","name":"F. Hoffmann-La Roche Ltd","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004337","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Epidemiology and Population Health"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.jeph.2026.203387","type":"journal-article","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T17:25:46Z","timestamp":1775755546000},"page":"203387","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting hepatocellular carcinoma in people with hepatitis B: a comparison between Cox proportional hazard and machine learning models"],"prefix":"10.1016","volume":"74","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1928-0263","authenticated-orcid":false,"given":"Cl\u00e9mence","family":"Ramier","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marie","family":"Guyomard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0164-7917","authenticated-orcid":false,"given":"Camelia","family":"Protopopescu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabrice","family":"Carrat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc","family":"Bourli\u00e8re","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrizia","family":"Carrieri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raquel","family":"Urena","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jeph.2026.203387_bib0001","unstructured":"International Agency for Research on Cancer. Cancer today n.d. http:\/\/gco.iarc.fr\/today\/home (accessed December 13, 2022)."},{"key":"10.1016\/j.jeph.2026.203387_bib0002","doi-asserted-by":"crossref","first-page":"1598","DOI":"10.1016\/j.jhep.2022.08.021","article-title":"Global burden of primary liver cancer in 2020 and predictions to 2040","volume":"77","author":"Rumgay","year":"2022","journal-title":"J Hepatol"},{"key":"10.1016\/j.jeph.2026.203387_bib0003","doi-asserted-by":"crossref","first-page":"3202","DOI":"10.3390\/biomedicines10123202","article-title":"Pathogenesis and current treatment strategies of hepatocellular carcinoma","volume":"10","author":"T\u00fcmen","year":"2022","journal-title":"Biomedicines"},{"key":"10.1016\/j.jeph.2026.203387_bib0004","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.jhep.2018.03.019","article-title":"EASL clinical practice guidelines: management of hepatocellular carcinoma","volume":"69","year":"2018","journal-title":"J Hepatol"},{"key":"10.1016\/j.jeph.2026.203387_bib0005","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1097\/CEJ.0000000000000428","article-title":"Worldwide incident hepatocellular carcinoma cases attributable to major risk factors","volume":"27","author":"Baecker","year":"2018","journal-title":"Eur J Cancer Prev"},{"key":"10.1016\/j.jeph.2026.203387_bib0006","article-title":"Statistical strategies for HCC risk prediction models in patients with chronic hepatitis B","volume":"7","author":"Yip","year":"2021","journal-title":"HR"},{"key":"10.1016\/j.jeph.2026.203387_bib0007","doi-asserted-by":"crossref","DOI":"10.1111\/liv.70065","article-title":"Behaviour-based predictive scores of hepatocellular carcinoma in people with chronic hepatitis B (ANRS CO22 HEPATHER)","volume":"45","author":"Ramier","year":"2025","journal-title":"Liver Int"},{"key":"10.1016\/j.jeph.2026.203387_bib0008","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.jhep.2021.09.025","article-title":"An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B","volume":"76","author":"Kim","year":"2022","journal-title":"J Hepatol"},{"key":"10.1016\/j.jeph.2026.203387_bib0009","doi-asserted-by":"crossref","first-page":"255","DOI":"10.4274\/MMJ.galenos.2022.39049","article-title":"Machine learning-based prediction of HBV-related hepatocellular carcinoma and detection of key candidate biomarkers","volume":"37","author":"Kucukakcali","year":"2022","journal-title":"Medeni Med J"},{"key":"10.1016\/j.jeph.2026.203387_bib0010","article-title":"Novel machine learning models outperform risk scores in predicting hepatocellular carcinoma in patients with chronic viral hepatitis","volume":"4","author":"Wong","year":"2022","journal-title":"JHEP Rep"},{"key":"10.1016\/j.jeph.2026.203387_bib0011","article-title":"Deep learning model for prediction of hepatocellular carcinoma in patients with HBV-related cirrhosis on antiviral therapy","volume":"2","author":"Nam","year":"2020","journal-title":"JHEP Rep"},{"key":"10.1016\/j.jeph.2026.203387_bib0012","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1111\/liv.15597","article-title":"A machine learning model for predicting hepatocellular carcinoma risk in patients with chronic hepatitis B","volume":"43","author":"Lee","year":"2023","journal-title":"Liver Int"},{"key":"10.1016\/j.jeph.2026.203387_bib0013","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1186\/s12874-022-01754-y","article-title":"Dementia risk prediction in individuals with mild cognitive impairment: a comparison of Cox regression and machine learning models","volume":"22","author":"Wang","year":"2022","journal-title":"BMC Med Res Methodol"},{"key":"10.1016\/j.jeph.2026.203387_bib0014","doi-asserted-by":"crossref","first-page":"e23","DOI":"10.5808\/gi.22036","article-title":"Comparison of survival prediction models for pancreatic cancer: cox model versus machine learning models","volume":"20","author":"Kim","year":"2022","journal-title":"Genom Inf"},{"key":"10.1016\/j.jeph.2026.203387_bib0015","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0297190","article-title":"Comparing the performance of statistical, machine learning, and deep learning algorithms to predict time-to-event: a simulation study for conversion to mild cognitive impairment","volume":"19","author":"Billichov\u00e1","year":"2024","journal-title":"PLoS One"},{"key":"10.1016\/j.jeph.2026.203387_bib0016","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1186\/s12874-020-01153-1","article-title":"Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques","volume":"20","author":"Kantidakis","year":"2020","journal-title":"BMC Med Res Methodol"},{"key":"10.1016\/j.jeph.2026.203387_bib0017","doi-asserted-by":"crossref","DOI":"10.1063\/5.0102305","article-title":"Prediction of survival models: a comparison between machine learning and Cox regression","volume":"2522","author":"Haynatzki","year":"2022","journal-title":"AIP Conf Proc"},{"key":"10.1016\/j.jeph.2026.203387_bib0018","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.jjcc.2021.11.006","article-title":"Machine learning-based prediction of 1-year mortality for acute coronary syndrome\u2730","volume":"79","author":"Hadanny","year":"2022","journal-title":"J Cardiol"},{"key":"10.1016\/j.jeph.2026.203387_bib0019","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.jhep.2017.03.021","article-title":"EASL 2017 clinical practice guidelines on the management of hepatitis B virus infection","volume":"67","year":"2017","journal-title":"J Hepatol"},{"key":"10.1016\/j.jeph.2026.203387_bib0020","unstructured":"D\u00e9finition - pauvret\u00e9 mon\u00e9taire \/seuil de pauvret\u00e9 \/ seuil de pauvret\u00e9 | insee n.d. https:\/\/www.insee.fr\/fr\/metadonnees\/definition\/c1653 (accessed June 15, 2021)."},{"key":"10.1016\/j.jeph.2026.203387_bib0021","unstructured":"L\u2019addiction \u00e0 l\u2019alcool - minist\u00e8re de la sant\u00e9 et de la pr\u00e9vention n.d.https:\/\/sante.gouv.fr\/prevention-en-sante\/addictions\/article\/l-addiction-a-l-alcool (accessed October 3, 2022)."},{"key":"10.1016\/j.jeph.2026.203387_bib0022","unstructured":"Body mass index - BMI n.d. https:\/\/www.euro.who.int\/en\/health-topics\/disease-prevention\/nutrition\/a-healthy-lifestyle\/body-mass-index-bmi (accessed May 9, 2022)."},{"key":"10.1016\/j.jeph.2026.203387_bib0023","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1214\/08-AOAS169","article-title":"Random survival forests","volume":"2","author":"Ishwaran","year":"2008","journal-title":"Ann Appl Stat"},{"key":"10.1016\/j.jeph.2026.203387_bib0024","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1093\/biostatistics\/kxj011","article-title":"Survival ensembles","volume":"7","author":"Hothorn","year":"2006","journal-title":"Biostatistics"},{"key":"10.1016\/j.jeph.2026.203387_bib0025","article-title":"An efficient training algorithm for kernel survival support vector machines","author":"P\u00f6lsterl","year":"2016","journal-title":"ArXiv"},{"key":"10.1016\/j.jeph.2026.203387_bib0026","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s12874-018-0482-1","article-title":"DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network","volume":"18","author":"Katzman","year":"2018","journal-title":"BMC Med Res Methodol"},{"key":"10.1016\/j.jeph.2026.203387_bib0027","doi-asserted-by":"crossref","first-page":"2543","DOI":"10.1001\/jama.1982.03320430047030","article-title":"Evaluating the yield of medical tests","volume":"247","author":"Harrell","year":"1982","journal-title":"JAMA"},{"key":"10.1016\/j.jeph.2026.203387_bib0028","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1080\/10543406.2020.1757692","article-title":"The inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic: an unbiased estimator in the presence of right censoring","volume":"30","author":"Dong","year":"2020","journal-title":"J Biopharm Stat"},{"key":"10.1016\/j.jeph.2026.203387_bib0029","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1111\/j.0006-341X.2000.00337.x","article-title":"Time-dependent ROC curves for censored survival data and a diagnostic marker","volume":"56","author":"Heagerty","year":"2000","journal-title":"Biometrics"},{"key":"10.1016\/j.jeph.2026.203387_bib0030","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"10.1016\/j.jeph.2026.203387_bib0031","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/s10115-013-0679-x","article-title":"Explaining prediction models and individual predictions with feature contributions","volume":"41","author":"\u0160trumbelj","year":"2014","journal-title":"Knowl Inf Syst"},{"key":"10.1016\/j.jeph.2026.203387_bib0032","doi-asserted-by":"crossref","DOI":"10.1002\/bimj.202100380","article-title":"Individual risk prediction: comparing random forests with Cox proportional-hazards model by a simulation study","volume":"65","author":"Baralou","year":"2023","journal-title":"Biom J"},{"key":"10.1016\/j.jeph.2026.203387_bib0033","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.ijmedinf.2018.05.006","article-title":"Comparison of variable selection methods for clinical predictive modeling","volume":"116","author":"Sanchez-Pinto","year":"2018","journal-title":"Int J Med Inf"},{"key":"10.1016\/j.jeph.2026.203387_bib0034","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1111\/ecog.00845","article-title":"What do we gain from simplicity versus complexity in species distribution models?","volume":"37","author":"Merow","year":"2014","journal-title":"Ecography"},{"key":"10.1016\/j.jeph.2026.203387_bib0035","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1186\/1471-2407-11-446","article-title":"How the risk of liver cancer changes after alcohol cessation: a review and meta-analysis of the current literature","volume":"11","author":"Heckley","year":"2011","journal-title":"BMC Cancer"},{"key":"10.1016\/j.jeph.2026.203387_bib0036","unstructured":"Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. Springer New York, NY; n.d."}],"container-title":["Journal of Epidemiology and Population Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2950433326002211?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2950433326002211?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T22:32:53Z","timestamp":1787005973000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2950433326002211"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":36,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["S2950433326002211"],"URL":"https:\/\/doi.org\/10.1016\/j.jeph.2026.203387","relation":{},"ISSN":["2950-4333"],"issn-type":[{"value":"2950-4333","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Predicting hepatocellular carcinoma in people with hepatitis B: a comparison between Cox proportional hazard and machine learning models","name":"articletitle","label":"Article Title"},{"value":"Journal of Epidemiology and Population Health","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jeph.2026.203387","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Masson SAS.","name":"copyright","label":"Copyright"}],"article-number":"203387"}}