A Transferable Belief Model Decision Support Tool over Complementary Clinical Conditions

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原文链接

NETL
Springer Nature

外文摘要：This paper presents an algorithm for decision support over two complementary clinical conditions given a large features data base。 The algorithm is mainly divided in two parts， the first one aims at identifying relevant features from a large dimension data base using a heuristic method based on a discriminating power。 The second part is a tool based on the Transferable Belief Model (TBM) which combines information extracted from the selected features to provide decision results with probabilities along with a result's consistency measure so that decision could be made carefully。 The proposed algorithm is tested on a downloaded feature data base。 The TBM based decision support tool showed consistent results w。r。t provided outcomes by combining data from two relevant features identified after using the heuristic feature ranking method。

外文关键词：

Belief functionsTransferable Belief Model Decision supportBio marker identificationData fusion

作者：

Abderraouf Hadj Henni、David Pasquier、Nacim Betrouni

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作者单位：

Cristal UMR CNRS 9189, University of Lillel, 59655 Villeneuve d'Ascq, France,Department of Radiation Oncology, Centre Oscar Lambert, 59000 Lille, France

INSERM U1171, University of Lille2, 59045 Lille, France

会议名称：

International work-conference on bioinformatics and biomedical engineering

会议地点：

Granada(ES)

会议母体文献：

Bioinformatics and biomedical engineering

页码：

409-420

出版时间：

2018

DOI：

10.1007/978-3-319-78759-6_37