Behind the scenes: A medical natural language processing project

被引：19

作者：

Wu, Joy T. ^{[1
,2
]}

Dernoncourt, Franck ^{[3
,4
]}

Gehrmann, Sebastian ^{[5
]}

Tyler, Patrick D. ^{[6
]}

Moseley, Edward T. ^{[7
]}

Carlson, Eric T. ^{[8
]}

Grant, David W. ^{[9
]}

Li, Yeran ^{[1
]}

Welt, Jonathan ^{[10
]}

Celi, Leo Anthony ^{[11
]}

机构：

[1] Harvard TH Chan Sch Publ Hlth, Cambridge, MA USA

[2] IBM Almaden Res Ctr, Med Sieve Radiol, San Jose, CA USA

[3] MIT, Comp Sci & Artificial Intelligence Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USA

[4] Adobe Res, San Jose, CA USA

[5] Harvard John A Paulson Sch Engn & Appl Sci, Cambridge, MA USA

[6] Beth Israel Deaconess Med Ctr, Dept Internal Med, Boston, MA 02215 USA

[7] Univ Massachusetts, Boston, MA 02125 USA

[8] Philips Res North Amer, Cambridge, MA USA

[9] Washington Univ, Sch Med, Div Plast & Reconstruct Surg, Dept Surg, St Louis, MO USA

[10] Massachusetts Gen Hosp, Wellman Ctr Photomed, Boston, MA 02114 USA

[11] MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA

来源：

INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS | 2018年 / 112卷

关键词：

Artificial intelligence in medicine; Natural language processing; Machine learning; Text analytics; Multidisciplinary teamwork; Cross-disciplinary research; Translational research; ARTIFICIAL-INTELLIGENCE; WATSON;

D O I：

10.1016/j.ijmedinf.2017.12.003

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 multi-disciplinary 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.

引用

页码：68 / 73

页数：6

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