Sparse Learning of the Disease Severity Score for High-Dimensional Data
المؤلفون المشاركون
Obradovic, Zoran
Stojkovic, Ivan
المصدر
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-12-18
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Learning disease severity scores automatically from collected measurements may aid in the quality of both healthcare and scientific understanding.
Some steps in that direction have been taken and machine learning algorithms for extracting scoring functions from data have been proposed.
Given the rapid increase in both quantity and diversity of data measured and stored, the large amount of information is becoming one of the challenges for learning algorithms.
In this work, we investigated the direction of the problem where the dimensionality of measured variables is large.
Learning the severity score in such cases brings the issue of which of measured features are relevant.
We have proposed a novel approach by combining desirable properties of existing formulations, which compares favorably to alternatives in accuracy and especially in the robustness of the learned scoring function.
The proposed formulation has a nonsmooth penalty that induces sparsity.
This problem is solved by addressing a dual formulation which is smooth and allows an efficient optimization.
The proposed approach might be used as an effective and reliable tool for both scoring function learning and biomarker discovery, as demonstrated by identifying a stable set of genes related to influenza symptoms’ severity, which are enriched in immune-related processes.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Stojkovic, Ivan& Obradovic, Zoran. 2017. Sparse Learning of the Disease Severity Score for High-Dimensional Data. Complexity،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1143364
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Stojkovic, Ivan& Obradovic, Zoran. Sparse Learning of the Disease Severity Score for High-Dimensional Data. Complexity No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1143364
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Stojkovic, Ivan& Obradovic, Zoran. Sparse Learning of the Disease Severity Score for High-Dimensional Data. Complexity. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1143364
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1143364
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