Deep learning facilitates the diagnosis of adult asthma.
Artificial intelligence
Asthma
Deep learning
Diagnosis
Support vector machine
Journal
Allergology international : official journal of the Japanese Society of Allergology
ISSN: 1440-1592
Titre abrégé: Allergol Int
Pays: England
ID NLM: 9616296
Informations de publication
Date de publication:
Oct 2019
Oct 2019
Historique:
received:
08
03
2019
revised:
12
04
2019
accepted:
23
04
2019
pubmed:
4
6
2019
medline:
12
2
2020
entrez:
3
6
2019
Statut:
ppublish
Résumé
We explored whether the use of deep learning to model combinations of symptom-physical signs and objective tests, such as lung function tests and the bronchial challenge test, would improve model performance in predicting the initial diagnosis of adult asthma when compared to the conventional machine learning diagnostic method. The data were obtained from the clinical records on prospective study of 566 adult out-patients who visited Kindai University Hospital for the first time with complaints of non-specific respiratory symptoms. Asthma was comprehensively diagnosed by specialists based on symptom-physical signs and objective tests. Model performance metrics were compared to logistic analysis, support vector machine (SVM) learning, and the deep neural network (DNN) model. For the diagnosis of adult asthma based on symptom-physical signs alone, the accuracy of the DNN model was 0.68, whereas that for the SVM was 0.60 and for the logistic analysis was 0.65. When adult asthma was diagnosed based on symptom-physical signs, biochemical findings, lung function tests, and the bronchial challenge test, the accuracy of the DNN model increased to 0.98 and was significantly higher than the 0.82 accuracy of the SVM and the 0.94 accuracy of the logistic analysis. DNN is able to better facilitate diagnosing adult asthma, compared with classical machine learnings, such as logistic analysis and SVM. The deep learning models based on symptom-physical signs and objective tests appear to improve the performance for diagnosing adult asthma.
Sections du résumé
BACKGROUND
BACKGROUND
We explored whether the use of deep learning to model combinations of symptom-physical signs and objective tests, such as lung function tests and the bronchial challenge test, would improve model performance in predicting the initial diagnosis of adult asthma when compared to the conventional machine learning diagnostic method.
METHODS
METHODS
The data were obtained from the clinical records on prospective study of 566 adult out-patients who visited Kindai University Hospital for the first time with complaints of non-specific respiratory symptoms. Asthma was comprehensively diagnosed by specialists based on symptom-physical signs and objective tests. Model performance metrics were compared to logistic analysis, support vector machine (SVM) learning, and the deep neural network (DNN) model.
RESULTS
RESULTS
For the diagnosis of adult asthma based on symptom-physical signs alone, the accuracy of the DNN model was 0.68, whereas that for the SVM was 0.60 and for the logistic analysis was 0.65. When adult asthma was diagnosed based on symptom-physical signs, biochemical findings, lung function tests, and the bronchial challenge test, the accuracy of the DNN model increased to 0.98 and was significantly higher than the 0.82 accuracy of the SVM and the 0.94 accuracy of the logistic analysis.
CONCLUSIONS
CONCLUSIONS
DNN is able to better facilitate diagnosing adult asthma, compared with classical machine learnings, such as logistic analysis and SVM. The deep learning models based on symptom-physical signs and objective tests appear to improve the performance for diagnosing adult asthma.
Identifiants
pubmed: 31153755
pii: S1323-8930(19)30061-9
doi: 10.1016/j.alit.2019.04.010
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
456-461Informations de copyright
Copyright © 2019 Japanese Society of Allergology. Production and hosting by Elsevier B.V. All rights reserved.