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Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.

Wang, Z; Shah, AD; Tate, AR; Denaxas, S; Shawe-Taylor, J; Hemingway, H; (2012) Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning. PLOS One , 7 (1) , Article e30412. 10.1371/journal.pone.0030412. Green open access

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Abstract

Electronic health records are invaluable for medical research, but much of the information is recorded as unstructured free text which is time-consuming to review manually.

Type: Article
Title: Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0030412
Publisher version: http://dx.doi.org/10.1371/journal.pone.0030412
Language: English
Additional information: © 2012 Wang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The research leading to these results has received funding from the Wellcome Trust (086091/Z/08/Z; http://www.wellcome.ac.uk/) and the National Institute of Health Research (RP-PG-0407-10314; http://www.nihr.ac.uk/) under the project CALIBER (Cardiovascular Disease Research Using Linked Bespoke Studies). This work was supported in part by the IST Programme of the European Community, under the PASCAL2 Network of Excellence. Anoop Shah is supported by a Wellcome Trust Clinical Research Training Fellowship (0938/30/Z/10/Z). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding was received for this study.
Keywords: Algorithms, Artificial Intelligence, Electronic Health Records, Female, Humans, Male, Ovarian Neoplasms
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1337295
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