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Medical i2b2 NLP smoking challenge: the A-Life system architecture and methodology.
J Am Med Inform Assoc. 2008 Jan-Feb;15(1):40-3. doi: 10.1197/jamia.M2438. Epub 2007 Oct 18.
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Mayo clinic NLP system for patient smoking status identification.
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Identifying patient smoking status from medical discharge records.
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Five-way smoking status classification using text hot-spot identification and error-correcting output codes.
J Am Med Inform Assoc. 2008 Jan-Feb;15(1):32-5. doi: 10.1197/jamia.M2434. Epub 2007 Oct 18.
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Description of a rule-based system for the i2b2 challenge in natural language processing for clinical data.
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A cloud-based approach to medical NLP.
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Use of semantic features to classify patient smoking status.
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Automated Detection of Substance-Use Status and Related Information from Clinical Text.
Sensors (Basel). 2022 Dec 8;22(24):9609. doi: 10.3390/s22249609.
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Improving the Utility of Tobacco-Related Problem List Entries Using Natural Language Processing.
AMIA Annu Symp Proc. 2021 Jan 25;2020:534-543. eCollection 2020.
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Determinants of Smoking and Quitting in HIV-Infected Individuals.
PLoS One. 2016 Apr 21;11(4):e0153103. doi: 10.1371/journal.pone.0153103. eCollection 2016.
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An automatic system to identify heart disease risk factors in clinical texts over time.
J Biomed Inform. 2015 Dec;58 Suppl(Suppl):S158-S163. doi: 10.1016/j.jbi.2015.09.002. Epub 2015 Sep 8.
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Practical implementation of an existing smoking detection pipeline and reduced support vector machine training corpus requirements.
J Am Med Inform Assoc. 2014 Jan-Feb;21(1):27-30. doi: 10.1136/amiajnl-2013-002090. Epub 2013 Aug 6.
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Unlocking Data for Clinical Research - The German i2b2 Experience.
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