Negation's not solved: generalizability versus optimizability in clinical natural language processing.
A review of published work in clinical natural language processing (NLP) may suggest that the negation detection task has been "solved." This work proposes that an optimizable solution does not equal a generalizable solution. We introduce a new machine learning-based Polarity Module for detecting negation in clinical text, and extensively compare its performance across domains. Using four manually annotated corpora of clinical text, we show that negation detection performance suffers when there is no in-domain development (for manual methods) or training data (for machine learning-based methods). Various factors (e.g., annotation guidelines, named entity characteristics, the amount of data, and lexical and syntactic context) play a role in making generalizability difficult, but none completely explains the phenomenon. Furthermore, generalizability remains challenging because it is unclear whether to use a single source for accurate data, combine all sources into a single model, or apply domain adaptation methods. The most reliable means to improve negation detection is to manually annotate in-domain training data (or, perhaps, manually modify rules); this is a strategy for optimizing performance, rather than generalizing it. These results suggest a direction for future work in domain-adaptive and task-adaptive methods for clinical NLP.
Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America; Oregon Health and Science University, Portland, Oregon, United States of America.
Stephen
S
Wu
Children's Hospital Boston Informatics Program, Harvard Medical School, Boston, Massachusetts, United States of America.
Timothy
T
Miller
Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America.
James
J
Masanz
Human Language Technology Department, The MITRE Corporation, Bedford, Massachusetts, United States of America.
Matt
M
Coarr
Group Health Research Institute, Seattle, Washington, United States of America.
Scott
S
Halgrim
Group Health Research Institute, Seattle, Washington, United States of America.
David
D
Carrell
Human Language Technology Department, The MITRE Corporation, Bedford, Massachusetts, United States of America.
Cheryl
C
Clark
D000465:Algorithms; D001185:Artificial Intelligence / Q000706:statistics & numerical data*; D015510:Clinical Medicine / Q000193:education; D006801:Humans; D009323:Natural Language Processing; D012660:Semantics; D013782:Textbooks as Topic; D018875:Vocabulary, Controlled