Combining Machine Learning with a Rule-Based Algorithm to Detect and Identify Related Entities of Documented Adverse Drug Reactions on Hospital Discharge Summaries

Hui Xing Tan, Chun Hwee Desmond Teo, Pei San Ang, Wei Ping Celine Loke, Mun Yee Tham, Siew Har Tan, Bee Leng Sally Soh, Pei Qin Belinda Foo, Zheng Jye Ling, Wei Luen James Yip, Yixuan Tang, Jisong Yang, Kum Hoe Anthony Tung and Sreemanee Raaj Dorajoo ()
Additional contact information
Hui Xing Tan: Health Sciences Authority
Chun Hwee Desmond Teo: Health Sciences Authority
Pei San Ang: Health Sciences Authority
Wei Ping Celine Loke: Health Sciences Authority
Mun Yee Tham: Health Sciences Authority
Siew Har Tan: Health Sciences Authority
Bee Leng Sally Soh: Health Sciences Authority
Pei Qin Belinda Foo: Health Sciences Authority
Zheng Jye Ling: Regional Health System Office, National University of Singapore, National University Health System
Wei Luen James Yip: National University Heart Centre
Yixuan Tang: National University of Singapore
Jisong Yang: National University of Singapore
Kum Hoe Anthony Tung: National University of Singapore
Sreemanee Raaj Dorajoo: Health Sciences Authority

Drug Safety, 2022, vol. 45, issue 8, No 3, 853-862

Abstract: Abstract Introduction Discharge summaries contain valuable information about adverse drug reactions, but their unstructured nature makes them challenging to analyse and use as a signal source for pharmacovigilance. Machine learning has shown promise in identifying discharge summaries that contain related drug-adverse event pairs but has fared relatively poorer in entity extraction. Methods A hybrid model is developed combining rule-based and machine learning algorithms using discharge summaries with the aim of maximising capture of related drug-adverse event pairs. The rule first identifies segments containing adverse event entities within a 100-character distance from a drug term; machine learning subsequently estimates the relatedness of the drug and adverse event entities contained. The approach is validated on four independent datasets that are temporally and geographically separated from model development data. The impact of restricted drug-adverse event pair detection on recall is evaluated by using two of the four validation datasets that do not impose rule-based restrictions to annotations. Results The hybrid model achieves a recall of 0.80 (fivefold cross validation), 0.80 (temporal) and 0.76 (geographical) on validation using datasets containing only pre-identified target text segments that fulfil the rule-based algorithm criteria. When tested on datasets that additionally contained drug-adverse event pairs not restricted by the rule-based criteria, recall of the model declines to 0.68 and 0.62 on temporally and geographically separated datasets, respectively. Conclusions The proposed hybrid model demonstrates reasonable generalisability on external validation. Rule-based restriction of the detection space results in an approximately 12–14% reduction in recall but improves identification of the related drug and adverse event terms.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40264-022-01196-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:drugsa:v:45:y:2022:i:8:d:10.1007_s40264-022-01196-x

Ordering information: This journal article can be ordered from
http://www.springer.com/adis/journal/40264

DOI: 10.1007/s40264-022-01196-x

Access Statistics for this article

Drug Safety is currently edited by Nitin Joshi

More articles in Drug Safety from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().