A Predictive Model to Classify Undifferentiated Fever Cases Based on Twenty-Four-Hour Continuous Tympanic Temperature Recording
المؤلفون المشاركون
Dakappa, Pradeepa H.
Prasad, Keerthana
Rao, Sathish B.
Bolumbu, Ganaraja
Bhat, Gopalkrishna K.
Mahabala, Chakrapani
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-6، 6ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-11-22
دولة النشر
مصر
عدد الصفحات
6
التخصصات الرئيسية
الملخص EN
Diagnosis of undifferentiated fever is a major challenging task to the physician which often remains undiagnosed and delays the treatment.
The aim of the study was to record and analyze a 24-hour continuous tympanic temperature and evaluate its utility in the diagnosis of undifferentiated fevers.
This was an observational study conducted in the Kasturba Medical College and Hospitals, Mangaluru, India.
A total of ninety-six (n=96) patients were presented with undifferentiated fever.
Their tympanic temperature was recorded continuously for 24 hours.
Temperature data were preprocessed and various signal characteristic features were extracted and trained in classification machine learning algorithms using MATLAB software.
The quadratic support vector machine algorithm yielded an overall accuracy of 71.9% in differentiating the fevers into four major categories, namely, tuberculosis, intracellular bacterial infections, dengue fever, and noninfectious diseases.
The area under ROC curve for tuberculosis, intracellular bacterial infections, dengue fever, and noninfectious diseases was found to be 0.961, 0.801, 0.815, and 0.818, respectively.
Good agreement was observed [kappa = 0.618 (p<0.001, 95% CI (0.498–0.737))] between the actual diagnosis of cases and the quadratic support vector machine learning algorithm.
The 24-hour continuous tympanic temperature recording with supervised machine learning algorithm appears to be a promising noninvasive and reliable diagnostic tool.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Dakappa, Pradeepa H.& Prasad, Keerthana& Rao, Sathish B.& Bolumbu, Ganaraja& Bhat, Gopalkrishna K.& Mahabala, Chakrapani. 2017. A Predictive Model to Classify Undifferentiated Fever Cases Based on Twenty-Four-Hour Continuous Tympanic Temperature Recording. Journal of Healthcare Engineering،Vol. 2017, no. 2017, pp.1-6.
https://search.emarefa.net/detail/BIM-1181072
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Dakappa, Pradeepa H.…[et al.]. A Predictive Model to Classify Undifferentiated Fever Cases Based on Twenty-Four-Hour Continuous Tympanic Temperature Recording. Journal of Healthcare Engineering No. 2017 (2017), pp.1-6.
https://search.emarefa.net/detail/BIM-1181072
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Dakappa, Pradeepa H.& Prasad, Keerthana& Rao, Sathish B.& Bolumbu, Ganaraja& Bhat, Gopalkrishna K.& Mahabala, Chakrapani. A Predictive Model to Classify Undifferentiated Fever Cases Based on Twenty-Four-Hour Continuous Tympanic Temperature Recording. Journal of Healthcare Engineering. 2017. Vol. 2017, no. 2017, pp.1-6.
https://search.emarefa.net/detail/BIM-1181072
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1181072
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر