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

Slater, Luke T, Bradlow, William, Motti, Dino Fa, Hoehndorf, Robert, Ball, Simon and Gkoutos, Georgios V (2021) A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text. Computers in biology and medicine, 130. p. 104216. ISSN 1879-0534. This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs

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Abstract

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.

Item Type: Article
Additional Information: This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs
Subjects: QA Mathematics. Computing
QW Microbiology. Immunology
QZ Pathology. Oncology
W Public health. Health statistics. Occupational health. Health education
Divisions: Clinical Support
Related URLs:
Depositing User: Jamie Edgar
Date Deposited: 03 Feb 2021 12:15
Last Modified: 03 Feb 2021 12:15
URI: http://www.repository.uhblibrary.co.uk/id/eprint/3932

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