Name
Abstract | ||
|---|---|---|
•Multidisciplinary team (MDT) model ensured a two-year long medical text processing (NLP) project was clinically relevant and technically solvable.•An academic setting helped connect diverse experts in databases, statistics, epidemiology, machine learning, NLP, engineering and medicine.•Up-front and regular time investment in cross-disciplinary learning and aligning research goals helped the MDT collaborated more effectively.•Domain knowledge from NLP and clinical experts was required to optimize the data sampling, annotation strategies and algorithm development.•Dynamic leadership, regular meetings and updates, clear task delegation, and an open learning environment helped maintain motivation and momentum. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ijmedinf.2017.12.003 | International Journal of Medical Informatics |
Keywords | Field | DocType |
Artificial intelligence in medicine,Natural language processing,Machine learning,Text analytics,Multidisciplinary teamwork,Cross-disciplinary research,Translational research | Health care,Teamwork,Clinical decision making,Multidisciplinary approach,Knowledge management,Natural language processing,Artificial intelligence,Economic shortage,Medicine | Journal |
Volume | ISSN | Citations |
112 | 1386-5056 | 0 |
PageRank | References | Authors |
0.34 | 19 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joy T. Wu | 1 | 0 | 0.34 |
| Franck Dernoncourt | 2 | 149 | 35.39 |
| Sebastian Gehrmann | 3 | 84 | 10.58 |
| Patrick D. Tyler | 4 | 5 | 1.42 |
| edward t moseley | 5 | 6 | 1.81 |
| Carlson, E. | 6 | 6 | 1.10 |
| David W. Grant | 7 | 0 | 0.34 |
| Yeran Li | 8 | 0 | 0.34 |
| Jonathan Welt | 9 | 0 | 0.34 |
| Leo Anthony Celi | 10 | 53 | 12.79 |