Improving ascertainment of suicidal ideation and suicide attempt with natural language processing.
Sci Rep
; 12(1): 15146, 2022 09 07.
Article
em En
| MEDLINE
| ID: mdl-36071081
ABSTRACT
Methods relying on diagnostic codes to identify suicidal ideation and suicide attempt in Electronic Health Records (EHRs) at scale are suboptimal because suicide-related outcomes are heavily under-coded. We propose to improve the ascertainment of suicidal outcomes using natural language processing (NLP). We developed information retrieval methodologies to search over 200 million notes from the Vanderbilt EHR. Suicide query terms were extracted using word2vec. A weakly supervised approach was designed to label cases of suicidal outcomes. The NLP validation of the top 200 retrieved patients showed high performance for suicidal ideation (area under the receiver operator curve [AUROC] 98.6, 95% confidence interval [CI] 97.1-99.5) and suicide attempt (AUROC 97.3, 95% CI 95.2-98.7). Case extraction produced the best performance when combining NLP and diagnostic codes and when accounting for negated suicide expressions in notes. Overall, we demonstrated that scalable and accurate NLP methods can be developed to identify suicidal behavior in EHRs to enhance prevention efforts, predictive models, and precision medicine.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Tentativa de Suicídio
/
Ideação Suicida
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos