Automated Spirometry Quality Assurance: Supervised Learning From Multiple Experts.
IEEE J Biomed Health Inform
; 22(1): 276-284, 2018 01.
Article
en En
| MEDLINE
| ID: mdl-28613188
ABSTRACT
Forced spirometry testing is gradually becoming available across different healthcare tiers including primary care. It has been demonstrated in earlier work that commercially available spirometers are not fully able to assure the quality of individual spirometry manoeuvres. Thus, a need to expand the availability of high-quality spirometry assessment beyond specialist pulmonary centres has arisen. In this paper, we propose a method to select and optimise a classifier using supervised learning techniques by learning from previously classified forced spirometry tests from a group of experts. Such a method is able to take into account the shape of the curve as an expert would during visual inspection. We evaluated the final classifier on a dataset put aside for evaluation yielding an area under the receiver operating characteristic curve of 0.88 and specificities of 0.91 and 0.86 for sensitivities of 0.60 and 0.82. Furthermore, other specificities and sensitivities along the receiver operating characteristic curve were close to the level of the experts when compared against each-other, and better than an earlier rules-based method assessed on the same dataset. We foresee key benefits in raising diagnostic quality, saving time, reducing cost, and also improving remote care and monitoring services for patients with chronic respiratory diseases in the future if a clinical decision support system with the encapsulated classifier is to be integrated into the work-flow of forced spirometry testing.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Espirometría
/
Sistemas de Apoyo a Decisiones Clínicas
/
Aprendizaje Automático Supervisado
Tipo de estudio:
Prognostic_studies
Límite:
Adult
/
Aged
/
Humans
/
Middle aged
Idioma:
En
Revista:
IEEE J Biomed Health Inform
Año:
2018
Tipo del documento:
Article