Deciphering clinical abbreviations with a privacy protecting machine learning system.
Nat Commun
; 13(1): 7456, 2022 12 02.
Article
em En
| MEDLINE
| ID: mdl-36460656
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Médicos
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Trombocitopenia
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Esclerose Múltipla
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article