Detection of Adverse Event Signals with Severity Grade Classification from Cancer Patient Narrative.
Stud Health Technol Inform
; 310: 554-558, 2024 Jan 25.
Article
en En
| MEDLINE
| ID: mdl-38269870
ABSTRACT
Adverse event (AE) management is crucial to improve anti-cancer treatment outcomes, but it is reported that some AE signals can be missed in clinical visits. Thus, monitoring AE signals seamlessly, including events outside hospitals, would be helpful for early intervention. Here we investigated how to detect AE signals from texts written by cancer patients themselves by developing deep-learning (DL) models to classify posts mentioning AEs according to severity grade, in order to focus on those that might need immediate treatment interventions. Using patient blogs written in Japanese by cancer patients as a data source, we built DL models based on three approaches, BERT, ELECTRA, and T5. Among these models, T5 showed the best F1 scores for both Grade ≥ 1 and ≥ 2 article classification tasks (0.85 and 0.53, respectively). This model might benefit patients by enabling earlier AE signal detection, thereby improving quality of life.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Asunto principal:
Calidad de Vida
/
Neoplasias
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Qualitative_research
Límite:
Humans
Idioma:
En
Revista:
Stud Health Technol Inform
/
Stud. health technol. inform.
/
Studies in health technology and informatics (Online)
Asunto de la revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Año:
2024
Tipo del documento:
Article
País de afiliación:
Japón