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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]
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Database: Engineering Source
Description
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]
ISSN:13865056
DOI:10.1016/j.ijmedinf.2017.12.003