Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections
المؤلفون المشاركون
Liang, Huiying
Zheng, Lingling
Lin, Fangqin
Zhu, Changxi
Liu, Guangjian
Wu, Xiaohui
Wu, Zhiyuan
Zheng, Jianbin
Xia, Huimin
Cai, Yi
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-07-27
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Sepsis is a high-mortality disease that is infected by bacteria, but pathogens in individual patients are difficult to diagnosis.
Metabolomic changes triggered by microbial activity provide us with the possibility of accurately identifying infection.
We adopted machine learning methods for training different classifiers with a clinical-metabolomic database from sepsis cases to identify the pathogen of sepsis.
Records of clinical indicators and concentration of metabolites were obtained for each patient upon their arrival at the hospital.
Machine learning algorithms were used in 100 patients with clear infection and corresponding 29 controls to select specific biosignatures to discriminate microorganism in septic patients.
The sensitivity, specificity, and AUC value of clinical and metabolomic characteristics in predicting diagnostic outcomes were determined at admission.
Our analyses demonstrate that the biosignatures selected by machine learning algorithms could have diagnostic value on the identification of infected patients and Gram-positive from Gram-negative; related AUC values were 0.94±0.054 and 0.80±0.085, respectively.
Pathway and blood disease enrichment analyses of clinical and metabolomic biomarkers among infected patients showed that sepsis disease was accompanied by abnormal nitrogen metabolism, cell respiratory disorder, and renal or intestinal failure.
The panel of selected clinical and metabolomic characteristics might be powerful biomarkers to discriminate patients with sepsis.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zheng, Lingling& Lin, Fangqin& Zhu, Changxi& Liu, Guangjian& Wu, Xiaohui& Wu, Zhiyuan…[et al.]. 2020. Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections. BioMed Research International،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1136359
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zheng, Lingling…[et al.]. Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections. BioMed Research International No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1136359
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zheng, Lingling& Lin, Fangqin& Zhu, Changxi& Liu, Guangjian& Wu, Xiaohui& Wu, Zhiyuan…[et al.]. Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1136359
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1136359
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