Use of artificial intelligence to identify data elements for The Japanese Orthopaedic Association National Registry from operative records.
J Orthop Sci
; 28(6): 1392-1399, 2023 Nov.
Article
em En
| MEDLINE
| ID: mdl-36163118
ABSTRACT
BACKGROUND:
The Japanese Orthopaedic Association National Registry (JOANR) was recently launched in Japan and is expected to improve the quality of medical care. However, surgeons must register ten detailed features for total hip arthroplasty, which is labor intensive. One possible solution is to use a system that automatically extracts information about the surgeries. Although it is not easy to extract features from an operative record consisting of free-text data, natural language processing has been used to extract features from operative records. This study aimed to evaluate the best natural language processing method for building a system that automatically detects some elements in the JOANR from the operative records of total hip arthroplasty.METHODS:
We obtained operative records of total hip arthroplasty (n = 2574) in three hospitals and targeted two items surgical approach and fixation technique. We compared the accuracy of three natural language processingmethods:
rule-based algorithms, machine learning, and bidirectional encoder representations from transformers (BERT).RESULTS:
In the surgical approach task, the accuracy of BERT was superior to that of the rule-based algorithm (99.6% vs. 93.6%, p < 0.001), comparable to machine learning. In the fixation technique task, the accuracy of BERT was superior to the rule-based algorithm and machine learning (96% vs. 74%, p < 0.0001 and 94%, p = 0.0004).CONCLUSIONS:
BERT is the most appropriate method for building a system that automatically detects the surgical approach and fixation technique.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Ortopedia
/
Inteligência Artificial
Tipo de estudo:
Risk_factors_studies
Limite:
Humans
País/Região como assunto:
Asia
Idioma:
En
Revista:
J Orthop Sci
Assunto da revista:
ORTOPEDIA
Ano de publicação:
2023
Tipo de documento:
Article
País de publicação:
JAPAN
/
JAPON
/
JAPÃO
/
JP