Sentiment analysis of medical record notes for lung cancer patients at the Department of Veterans Affairs.
PLoS One
; 18(1): e0280931, 2023.
Article
en En
| MEDLINE
| ID: mdl-36696437
ABSTRACT
Natural language processing of medical records offers tremendous potential to improve the patient experience. Sentiment analysis of clinical notes has been performed with mixed results, often highlighting the issue that dictionary ratings are not domain specific. Here, for the first time, we re-calibrate the labMT sentiment dictionary on 3.5M clinical notes describing 10,000 patients diagnosed with lung cancer at the Department of Veterans Affairs. The sentiment score of notes was calculated for two years after date of diagnosis and evaluated against a lab test (platelet count) and a combination of data points (treatments). We found that the oncology specific labMT dictionary, after re-calibration for the clinical oncology domain, produces a promising signal in notes that can be detected based on a comparative analysis to the aforementioned parameters.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Veteranos
/
Neoplasias Pulmonares
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
En
Revista:
PLoS One
Asunto de la revista:
CIENCIA
/
MEDICINA
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos