Behind the scenes: A medical natural language processing project

Joy T Wu; Franck Dernoncourt; Sebastian Gehrmann; Patrick D Tyler; Edward T Moseley; Eric T Carlson; David W Grant; Yeran Li; Jonathan Welt; Leo Anthony Celi

doi:10.1016/j.ijmedinf.2017.12.003

Behind the scenes: A medical natural language processing project

Int J Med Inform. 2018 Apr:112:68-73. doi: 10.1016/j.ijmedinf.2017.12.003. Epub 2017 Dec 9.

Authors

Affiliations

¹ Harvard T.H. Chan School of Public Health, Cambridge, MA, USA; Medical Sieve Radiology, IBM Almaden Research Center, San Jose, CA, USA. Electronic address: joytywu@gmail.com.
² Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Adobe Research, San Jose, CA, USA.
³ Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA, USA.
⁴ Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
⁵ University of Massachusetts, Boston, MA, USA.
⁶ Philips Research North America, Cambridge, MA, USA.
⁷ Department of Surgery, Division of Plastic and Reconstructive Surgery, Washington University School of Medicine, St. Louis, MO, USA.
⁸ Harvard T.H. Chan School of Public Health, Cambridge, MA, USA.
⁹ Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁰ Massachusetts Institute of Technology, Cambridge, MA, USA.

PMID: 29500024
DOI: 10.1016/j.ijmedinf.2017.12.003

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.

Keywords: Artificial intelligence in medicine; Cross-disciplinary research; Machine learning; Multidisciplinary teamwork; Natural language processing; Text analytics; Translational research.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Artificial Intelligence*
Clinical Decision-Making*
Humans
Machine Learning*
Natural Language Processing*

Grants and funding

R01 EB017205/EB/NIBIB NIH HHS/United States