Paper Info
Title
Graph-Based Multi-Modality Learning for Clinical Decision Support
Abstract
The task of clinical decision support (CDS) involves retrieval and ranking of medical journal articles for medical records of diagnosis, test or treatment. Previous studies on this task are based on bag-of-words representations of document texts and general retrieval models. In this paper, we propose to use the paragraph vector technique to learn the latent semantic representation of texts and treat the latent semantic representations and the original bag-of-words representations as two different modalities. We then propose to use the graph-based multi-modality learning algorithm for document re-ranking. Experimental results on two TREC-CDS benchmark datasets demonstrate the excellent performance of our proposed approach.
Year
DOI
Venue
2016
10.1145/2983323.2983880
ACM International Conference on Information and Knowledge Management
Keywords
Field
DocType
Clinical Decision Support,Paragraph Vector,Graph-based Multi-Modality Learning,TREC
Modalities,Data mining,Graph,Ranking,Information retrieval,Computer science,Paragraph,Natural language processing,Artificial intelligence,Clinical decision support system,Semantic representation
Conference
Citations 
PageRank 
References 
2
0.37
3
Authors
2
Name
Order
Citations
PageRank
Ziwei Zheng120.37
Xiaojun Wan21685125.70