A homogeneous ensemble method for predicting gastric cancer based on gastroscopy reports Ding, Shuai, Hu, Shikang, Pan, Jinxin, Li, Xiaojian, Li, Gang and Liu, Xiao 2020, A homogeneous ensemble method for predicting gastric cancer based on gastroscopy reports, Expert systems, vol. 37, no. 3, Special Issue: New data envelopment analysis models for assessing sustainability: Part 1: A dynamic data envelopment analysis approach, pp. 1-14, doi: 10.1111/exsy.12499. Attached Files Name Description MIMEType Size Downloads Title A homogeneous ensemble method for predicting gastric cancer based on gastroscopy reports Author(s) Ding, Shuai Hu, Shikang Pan, Jinxin Li, Xiaojian Li, Gang orcid.org/0000-0003-1583-641X Liu, Xiao orcid.org/0000-0001-8400-5754 Journal name Expert systems Volume number 37 Issue number 3 Season Special Issue: New data envelopment analysis models for assessing sustainability: Part 1: A dynamic data envelopment analysis approach Article ID e12499 Start page 1 End page 14 Total pages 14 Publisher Wiley Place of publication Chichester, Eng. Publication date 2020-06 ISSN 0266-4720 1468-0394 Keyword(s) Science & Technology Technology Computer Science, Artificial Intelligence Computer Science, Theory & Methods Computer Science clinical decision support gastric cancer inference homogeneous ensemble natural language processing DIAGNOSIS CLASSIFICATION Language eng DOI 10.1111/exsy.12499 Indigenous content off Field of Research 0801 Artificial Intelligence and Image Processing 1702 Cognitive Sciences HERDC Research category C1 Refereed article in a scholarly journal Persistent URL http://hdl.handle.net/10536/DRO/DU:30133356 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 02 Jan 2020, 12:30:33 EST Thu, 02 Jan 2020, 12:31:23 EST Fri, 03 Jan 2020, 04:02:21 EST Fri, 03 Jan 2020, 05:04:57 EST Sat, 11 Jan 2020, 01:32:07 EST Tue, 04 Feb 2020, 12:53:02 EST Tue, 04 Feb 2020, 12:57:02 EST Mon, 15 Jun 2020, 11:58:10 EST Thu, 17 Sep 2020, 11:31:03 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 147 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Thu, 02 Jan 2020, 12:31:23 EST

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