{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T05:59:25Z","timestamp":1790402365662,"version":"4.1.0"},"reference-count":30,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2012,8,3]],"date-time":"2012-08-03T00:00:00Z","timestamp":1343952000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Viral Hepatitis"],"published-print":{"date-parts":[[2013,4]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Model for end\u2010stage liver disease (<jats:styled-content style=\"fixed-case\">MELD<\/jats:styled-content>) scoring was initiated using traditional statistical technique by assuming a linear relationship between clinical features, but most phenomena in a clinical situation are not linearly related. The aim of this study was to predict 3\u2010month mortality risk of acute\u2010on\u2010chronic hepatitis <jats:styled-content style=\"fixed-case\">B<\/jats:styled-content> liver failure (<jats:styled-content style=\"fixed-case\">ACHBLF<\/jats:styled-content>) on an individual patient level using an artificial neural network (<jats:styled-content style=\"fixed-case\">ANN<\/jats:styled-content>) system. The <jats:styled-content style=\"fixed-case\">ANN<\/jats:styled-content> model was built using data from 402 consecutive patients with <jats:styled-content style=\"fixed-case\">ACHBLF<\/jats:styled-content>. It was trained to predict 3\u2010month mortality by the data of 280 patients and validated by the remaining 122 patients. The area under the curve of receiver operating characteristic (<jats:styled-content style=\"fixed-case\">AUROC<\/jats:styled-content>) was calculated for <jats:styled-content style=\"fixed-case\">ANN<\/jats:styled-content> and <jats:styled-content style=\"fixed-case\">MELD<\/jats:styled-content>\u2010based scoring systems. The following variables age (<jats:italic>P<\/jats:italic>\u00a0&lt;\u00a00.001), prothrombin activity (<jats:italic>P\u00a0<\/jats:italic>&lt;<jats:italic>\u00a0<\/jats:italic>0.001), serum sodium (<jats:italic>P<\/jats:italic> &lt; 0.001), total bilirubin (<jats:italic>P\u00a0<\/jats:italic>=<jats:italic>\u00a0<\/jats:italic>0.015), hepatitis <jats:styled-content style=\"fixed-case\">B<\/jats:styled-content> e antigen positivity rate (<jats:italic>P\u00a0<\/jats:italic>&lt;<jats:italic>\u00a0<\/jats:italic>0.001) and haemoglobin (<jats:italic>P\u00a0<\/jats:italic>&lt;<jats:italic>\u00a0<\/jats:italic>0.001) were significantly related to the prognosis of <jats:styled-content style=\"fixed-case\">ACHBLF<\/jats:styled-content> and were selected to build the <jats:styled-content style=\"fixed-case\">ANN<\/jats:styled-content>. The <jats:styled-content style=\"fixed-case\">ANN<\/jats:styled-content> performed significantly better than <jats:styled-content style=\"fixed-case\">MELD<\/jats:styled-content>\u2010based scoring systems both in the training cohort (<jats:styled-content style=\"fixed-case\">AUROC<\/jats:styled-content>\u00a0=\u00a00.869 <jats:italic>vs<\/jats:italic> 0.667, 0.591, 0.643, 0.571 and 0.577; <jats:italic>P\u00a0<\/jats:italic>&lt;<jats:italic>\u00a0<\/jats:italic>0.001, respectively) and in the validation cohort (<jats:styled-content style=\"fixed-case\">AUROC<\/jats:styled-content>\u00a0=\u00a00.765 <jats:italic>vs<\/jats:italic> 0.599, 0.563, 0.601, 0.521 and 0.540; <jats:italic>P\u00a0<\/jats:italic>\u2264<jats:italic>\u00a0<\/jats:italic>0.006, respectively). Thus, the <jats:styled-content style=\"fixed-case\">ANN<\/jats:styled-content> model was shown to be more accurate in predicting 3\u2010month mortality of <jats:styled-content style=\"fixed-case\">ACHBLF<\/jats:styled-content> than <jats:styled-content style=\"fixed-case\">MELD<\/jats:styled-content>\u2010based scoring systems.<\/jats:p>","DOI":"10.1111\/j.1365-2893.2012.01647.x","type":"journal-article","created":{"date-parts":[[2012,8,3]],"date-time":"2012-08-03T14:47:34Z","timestamp":1344005254000},"page":"248-255","source":"Crossref","is-referenced-by-count":46,"title":["A model to predict 3\u2010month mortality risk of acute\u2010on\u2010chronic hepatitis <scp>B<\/scp> liver failure using artificial neural network"],"prefix":"10.1111","volume":"20","author":[{"given":"M.\u2010H.","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Infection and Liver Diseases Liver Research Center The First Affiliated Hospital of Wenzhou Medical College Wenzhou 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