8,472 research outputs found
A Relation Extraction Approach for Clinical Decision Support
In this paper, we investigate how semantic relations between concepts
extracted from medical documents can be employed to improve the retrieval of
medical literature. Semantic relations explicitly represent relatedness between
concepts and carry high informative power that can be leveraged to improve the
effectiveness of retrieval functionalities of clinical decision support
systems. We present preliminary results and show how relations are able to
provide a sizable increase of the precision for several topics, albeit having
no impact on others. We then discuss some future directions to minimize the
impact of negative results while maximizing the impact of good results.Comment: 4 pages, 1 figure, DTMBio-KMH 2018, in conjunction with ACM 27th
Conference on Information and Knowledge Management (CIKM), October 22-26
2018, Lingotto, Turin, Ital
Deep learning for extracting protein-protein interactions from biomedical literature
State-of-the-art methods for protein-protein interaction (PPI) extraction are
primarily feature-based or kernel-based by leveraging lexical and syntactic
information. But how to incorporate such knowledge in the recent deep learning
methods remains an open question. In this paper, we propose a multichannel
dependency-based convolutional neural network model (McDepCNN). It applies one
channel to the embedding vector of each word in the sentence, and another
channel to the embedding vector of the head of the corresponding word.
Therefore, the model can use richer information obtained from different
channels. Experiments on two public benchmarking datasets, AIMed and BioInfer,
demonstrate that McDepCNN compares favorably to the state-of-the-art
rich-feature and single-kernel based methods. In addition, McDepCNN achieves
24.4% relative improvement in F1-score over the state-of-the-art methods on
cross-corpus evaluation and 12% improvement in F1-score over kernel-based
methods on "difficult" instances. These results suggest that McDepCNN
generalizes more easily over different corpora, and is capable of capturing
long distance features in the sentences.Comment: Accepted for publication in Proceedings of the 2017 Workshop on
Biomedical Natural Language Processing, 10 pages, 2 figures, 6 table
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