عنوان انگلیسی مقاله:
Deep learning facilitates the diagnosis of adult asthma
ترجمه فارسی عنوان مقاله:
تسهیلات یادگیری عمیق در تشخیص آسم بزرگسالان
Sciencedirect - Elsevier - Allergology International, 68 (2019) 456-461: doi:10:1016/j:alit:2019:04:010
Katsuyuki Tomita a, *, Ryota Nagao a, Hirokazu Touge a, Tomoyuki Ikeuchi a, Hiroyuki Sano b, Akira Yamasaki c, Yuji Tohda
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: The data were obtained from the clinical records on prospective study of 566 adult outpatients
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: 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: 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
Keywords: Artificial intelligence | Asthma | Deep learning | Diagnosis | Support vector machine