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Behind the scenes: A medical natural language processing project.

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Title: Behind the scenes: A medical natural language processing project.
Authors: Wu, Joy T.1,2 joytywu@gmail.com, Dernoncourt, Franck3,4, Gehrmann, Sebastian5, Tyler, Patrick D6, Moseley, Edward T7, Carlson, Eric T8, Grant, David W9, Li, Yeran1, Welt, Jonathan10, Celi, Leo Anthony11
Source: International Journal of Medical Informatics. Apr2018, Vol. 112, p68-73. 6p.
Subjects: Natural language processing, Artificial intelligence, Medical informatics, Machine learning, Algorithms
Abstract: Advancement of Artificial Intelligence (AI) capabilities in medicine can help address many pressing problems in healthcare. However, AI research endeavors in healthcare may not be clinically relevant, may have unrealistic expectations, or may not be explicit enough about their limitations. A diverse and well-functioning multidisciplinary team (MDT) can help identify appropriate and achievable AI research agendas in healthcare, and advance medical AI technologies by developing AI algorithms as well as addressing the shortage of appropriately labeled datasets for machine learning. In this paper, our team of engineers, clinicians and machine learning experts share their experience and lessons learned from their two-year-long collaboration on a natural language processing (NLP) research project. We highlight specific challenges encountered in cross-disciplinary teamwork, dataset creation for NLP research, and expectation setting for current medical AI technologies. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Medical Informatics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 128227031
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Behind the scenes: A medical natural language processing project.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Medical+Informatics%22">International Journal of Medical Informatics</searchLink>. Apr2018, Vol. 112, p68-73. 6p.
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  Data: Advancement of Artificial Intelligence (AI) capabilities in medicine can help address many pressing problems in healthcare. However, AI research endeavors in healthcare may not be clinically relevant, may have unrealistic expectations, or may not be explicit enough about their limitations. A diverse and well-functioning multidisciplinary team (MDT) can help identify appropriate and achievable AI research agendas in healthcare, and advance medical AI technologies by developing AI algorithms as well as addressing the shortage of appropriately labeled datasets for machine learning. In this paper, our team of engineers, clinicians and machine learning experts share their experience and lessons learned from their two-year-long collaboration on a natural language processing (NLP) research project. We highlight specific challenges encountered in cross-disciplinary teamwork, dataset creation for NLP research, and expectation setting for current medical AI technologies. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Medical Informatics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=128227031
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        Value: 10.1016/j.ijmedinf.2017.12.003
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        Text: English
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      – SubjectFull: Medical informatics
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      – SubjectFull: Machine learning
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      – SubjectFull: Algorithms
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              Text: Apr2018
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