NLM’s System Description for the Fourth i2b2/VA Challenge

By Dina Demner-Fushman, Emilia Apostolova, Rezarta, François-Michel Lang, James G. Mork, Aurélie Névéol, Sonya E. Shooshan, Matthew S. Simpson, Alan R. Aronson. 2010

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yuan Luo
2. Ozlem Uzuner
3. Peter Szolovits
4. Yu Cheng
5. Justin B. Starren
6. Brett R. South
7. Shuying Shen
8. Scott L. DuVall

Citations in the corpus

• 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text (2011, methodology): “21 25 39 Anick et al ’ s system 39 used lists of n-grams with speci fi c semantics, Divita et al 37 augmented the reference standard for the least prevalent relation classes, Demner-Fushman et al 35 utilized UMLS CUIs 43 and exercised feature reduction through cross-validation, and Grouin et al 36 co”
• Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes (2018, background): “Others supplemented their machine learning systems with rules that capture linguistic patterns of relations.(23,25,26) One of the top-performing teams(17) performed a follow-up study by employing a composite kernel–based model that consists of concept kernels, connection kernels, and tree kernels in”