Deciphering clinical abbreviations with a privacy protecting machine learning system

Alvin Rajkomar (), Eric Loreaux, Yuchen Liu, Jonas Kemp, Benny Li, Ming-Jun Chen, Yi Zhang, Afroz Mohiuddin and Juraj Gottweis
Additional contact information
Alvin Rajkomar: Google
Eric Loreaux: Google
Yuchen Liu: Google
Jonas Kemp: Google
Benny Li: Google
Ming-Jun Chen: Google
Yi Zhang: Google
Afroz Mohiuddin: Google
Juraj Gottweis: Google

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: Abstract Physicians write clinical notes with abbreviations and shorthand that are difficult to decipher. Abbreviations can be clinical jargon (writing “HIT” for “heparin induced thrombocytopenia”), ambiguous terms that require expertise to disambiguate (using “MS” for “multiple sclerosis” or “mental status”), or domain-specific vernacular (“cb” for “complicated by”). Here we train machine learning models on public web data to decode such text by replacing abbreviations with their meanings. We report a single translation model that simultaneously detects and expands thousands of abbreviations in real clinical notes with accuracies ranging from 92.1%-97.1% on multiple external test datasets. The model equals or exceeds the performance of board-certified physicians (97.6% vs 88.7% total accuracy). Our results demonstrate a general method to contextually decipher abbreviations and shorthand that is built without any privacy-compromising data.

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

Downloads: (external link)
https://www.nature.com/articles/s41467-022-35007-9 Abstract (text/html)

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:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35007-9

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-022-35007-9

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

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