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Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
Automatically extracting useful information from electronic medical reco...
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Probabilistic Logic Neural Networks for Reasoning
Knowledge graph reasoning, which aims at predicting the missing facts th...
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Hierarchical Character Embeddings: Learning Phonological and Semantic Representations in Languages of Logographic Origin using Recursive Neural Networks
Logographs (Chinese characters) have recursive structures (i.e. hierarch...
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Feature Weight Tuning for Recursive Neural Networks
This paper addresses how a recursive neural network model can automatica...
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Literature Triage on Genomic Variation Publications by Knowledge-enhanced Multi-channel CNN
Background: To investigate the correlation between genomic variation and...
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Disease Labeling via Machine Learning is NOT quite the same as Medical Diagnosis
A key step in medical diagnosis is giving the patient a universally reco...
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EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning
Objective: Electronic medical records (EMRs) contain an amount of medica...
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Medical Knowledge Embedding Based on Recursive Neural Network for Multi-Disease Diagnosis
The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic representation enables quantitative reasoning with statistical probability, it is difficult to utilize with machine learning models as they perform numerical operations. In contrast, knowledge embedding (i.e., high-dimensional and continuous vectors) is a feasible approach to complex reasoning that can not only retain the semantic information of knowledge but also establish the quantifiable relationship among them. In this paper, we propose recursive neural knowledge network (RNKN), which combines medical knowledge based on first-order logic with recursive neural network for multi-disease diagnosis. After RNKN is efficiently trained from manually annotated Chinese Electronic Medical Records (CEMRs), diagnosis-oriented knowledge embeddings and weight matrixes are learned. Experimental results verify that the diagnostic accuracy of RNKN is superior to that of some classical machine learning models and Markov logic network (MLN). The results also demonstrate that the more explicit the evidence extracted from CEMRs is, the better is the performance achieved. RNKN gradually exhibits the interpretation of knowledge embeddings as the number of training epochs increases.
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