A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text

Luke T Slater, William Bradlow, Dino Fa Motti, Robert Hoehndorf, Simon Ball, Georgios V Gkoutos

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1 Citation (Scopus)
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Negation detection is an important task in biomedical text mining. Particularly in clinical settings, it is of critical importance to determine whether findings mentioned in text are present or absent. Rule-based negation detection algorithms are a common approach to the task, and more recent investigations have resulted in the development of rule-based systems utilising the rich grammatical information afforded by typed dependency graphs. However, interacting with these complex representations inevitably necessitates complex rules, which are time-consuming to develop and do not generalise well. We hypothesise that a heuristic approach to determining negation via dependency graphs could offer a powerful alternative. We describe and implement an algorithm for negation detection based on grammatical distance from a negatory construct in a typed dependency graph. To evaluate the algorithm, we develop two testing corpora comprised of sentences of clinical text extracted from the MIMIC-III database and documents related to hypertrophic cardiomyopathy patients routinely collected at University Hospitals Birmingham NHS trust. Gold-standard validation datasets were built by a combination of human annotation and examination of algorithm error. Finally, we compare the performance of our approach with four other rule-based algorithms on both gold-standard corpora. The presented algorithm exhibits the best performance by f-measure over the MIMIC-III dataset, and a similar performance to the syntactic negation detection systems over the HCM dataset. It is also the fastest of the dependency-based negation systems explored in this study. Our results show that while a single heuristic approach to dependency-based negation detection is ignorant to certain advanced cases, it nevertheless forms a powerful and stable method, requiring minimal training and adaptation between datasets. As such, it could present a drop-in replacement or augmentation for many-rule negation approaches in clinical text-mining pipelines, particularly for cases where adaptation and rule development is not required or possible.

Original languageEnglish
Article number104216
Number of pages8
JournalComputers in Biology and Medicine
Early online date16 Jan 2021
Publication statusPublished - Mar 2021

Bibliographical note

Funding Information:
GVG and LTS acknowledge support from support from the NIHR Birmingham ECMC , NIHR Birmingham SRMRC , Nanocommons H2020-EU ( 731032 ) and the NIHR Birmingham Biomedical Research Centre and the MRC HDR UK ( HDRUK/CFC/01 ), an initiative funded by UK Research and Innovation , Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health.

Funding Information:
RH and GVG were supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3790-01-01 .

Publisher Copyright:
© 2021 The Author(s)


  • Text mining
  • negation
  • detection
  • context
  • disambiguation
  • clinical information extraction

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


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