Exploratory study on classification of chronic obstructive pulmonary disease combining multi-stage feature fusion and machine learning.
BMC Med Inform Decis Mak
; 21(1): 348, 2021 12 14.
Article
em En
| MEDLINE
| ID: mdl-34906123
ABSTRACT
BACKGROUND:
Due to the complexity and high heterogeneity of the acute exacerbation of chronic obstructive pulmonary disease (AECOPD), the guidelines (global initiative for chronic obstructive, GOLD) is unable to fully guide the treatment of AECOPD.OBJECTIVES:
To provide a rapid treatment in line with the development of the AECOPD after admission. In this paper, we propose a multi-stage feature fusion (MSFF) framework combining machine learning to track the diseases deterioration risk of the AECOPD.METHODS:
First, we identify 408 AECOPD patients as the study population. Then, feature segment and fusion methods are applied to generate the phased data set. Finally, human studies are designed to evaluate the performance of the MSFF framework.RESULTS:
The experimental results show that the proposed framework is potential to obtain the full-process tracking of deterioration risk for the AECOPD patients. The proposed MSFF framework achieves a higher overall accuracy average and F1 scores than the four physician groups i.e., IM, Surgery, Emergency, and ICU.CONCLUSIONS:
The proposed MSFF model may serve as a useful disease tracking tool to estimate the deterioration risk at each stage, and finally achieve the disease monitoring and management for AECOPD patients.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Doença Pulmonar Obstrutiva Crônica
Tipo de estudo:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
BMC Med Inform Decis Mak
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
China