{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:27:31Z","timestamp":1774538851263,"version":"3.50.1"},"reference-count":14,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T00:00:00Z","timestamp":1611964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000066","name":"NIEHS","doi-asserted-by":"publisher","award":["K01ES025434"],"award-info":[{"award-number":["K01ES025434"]}],"id":[{"id":"10.13039\/100000066","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NIH Big Data to Knowledge (BD2K) initiative","award":["R01 LM012373"],"award-info":[{"award-number":["R01 LM012373"]}]},{"name":"NIH Big Data to Knowledge (BD2K) initiative","award":["R01 LM012907"],"award-info":[{"award-number":["R01 LM012907"]}]},{"DOI":"10.13039\/100000092","name":"NLM","doi-asserted-by":"publisher","award":["R01 HD084633"],"award-info":[{"award-number":["R01 HD084633"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000071","name":"NICHD","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000071","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The University of Michigan Office of Research"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Cox-nnet is a neural-network-based prognosis prediction method, originally applied to genomics data. Here, we propose the version 2 of Cox-nnet, with significant improvement on efficiency and interpretability, making it suitable to predict prognosis based on large-scale population data, including those electronic medical records (EMR) datasets. We also add permutation-based feature importance scores and the direction of feature coefficients. When applied on a kidney transplantation dataset, Cox-nnet v2.0 reduces the training time of Cox-nnet up to 32-folds (n\u00a0=10\u00a0000) and achieves better prediction accuracy than Cox-PH (P&amp;lt;0.05). It also achieves similarly superior performance on a publicly available SUPPORT data (n=8000). The high efficiency and accuracy make Cox-nnet v2.0 a desirable method for survival prediction in large-scale EMR data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Cox-nnet v2.0 is freely available to the public at https:\/\/github.com\/lanagarmire\/Cox-nnet-v2.0.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab046","type":"journal-article","created":{"date-parts":[[2021,1,23]],"date-time":"2021-01-23T15:58:04Z","timestamp":1611417484000},"page":"2772-2774","source":"Crossref","is-referenced-by-count":15,"title":["Cox-nnet v2.0: improved neural-network-based survival prediction extended to large-scale EMR data"],"prefix":"10.1093","volume":"37","author":[{"given":"Di","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Biostatistics, University of Michigan , Ann Arbor, MI 48109, USA"}]},{"given":"Zheng","family":"Jing","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Michigan , Ann Arbor, MI 48109, USA"}]},{"given":"Kevin","family":"He","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Michigan , Ann Arbor, MI 48109, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1672-6917","authenticated-orcid":false,"given":"Lana X.","family":"Garmire","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, MI 48109, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,1,30]]},"reference":[{"key":"2023051609170266500_btab046-B1","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. 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