دانلود مقاله انگلیسی رایگان:تسهیلات یادگیری عمیق در تشخیص آسم بزرگسالان - 2019
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  • Deep learning facilitates the diagnosis of adult asthma Deep learning facilitates the diagnosis of adult asthma
    Deep learning facilitates the diagnosis of adult asthma

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Deep learning facilitates the diagnosis of adult asthma


    ترجمه فارسی عنوان مقاله:

    تسهیلات یادگیری عمیق در تشخیص آسم بزرگسالان


    منبع:

    Sciencedirect - Elsevier - Allergology International, 68 (2019) 456-461: doi:10:1016/j:alit:2019:04:010


    نویسنده:

    Katsuyuki Tomita a, *, Ryota Nagao a, Hirokazu Touge a, Tomoyuki Ikeuchi a, Hiroyuki Sano b, Akira Yamasaki c, Yuji Tohda


    چکیده انگلیسی:

    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


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 6
    حجم فایل: 922 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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