Erratum: inverting a sum of matrices
SIAM Review
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Proceedings of the 11th international conference on World Wide Web
Random walk with restart: fast solutions and applications
Knowledge and Information Systems
Accuracy estimate and optimization techniques for SimRank computation
Proceedings of the VLDB Endowment
Automatic image tagging as a random walk with priors on the canonical correlation subspace
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Index Design for Dynamic Personalized PageRank
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Ranking-based clustering of heterogeneous information networks with star network schema
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Annotating and recognising named entities in clinical notes
ACLstudent '09 Proceedings of the ACL-IJCNLP 2009 Student Research Workshop
Fast computation of SimRank for static and dynamic information networks
Proceedings of the 13th International Conference on Extending Database Technology
Recognizing medication related entities in hospital discharge summaries using support vector machine
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Estimating PageRank on graph streams
Journal of the ACM (JACM)
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As an integral part of Electronic Health Records (EHRs), clinical notes pose special challenges for analyzing EHRs due to their unstructured nature. In this paper, we present a general mining framework SympGraph for modeling and analyzing symptom relationships in clinical notes. A SympGraph has symptoms as nodes and co-occurrence relations between symptoms as edges, and can be constructed automatically through extracting symptoms over sequences of clinical notes for a large number of patients. We present an important clinical application of SympGraph: symptom expansion, which can expand a given set of symptoms to other related symptoms by analyzing the underlying SympGraph structure. We further propose a matrix update algorithm which provides a significant computational saving for dynamic updates to the graph. Comprehensive evaluation on 1 million longitudinal clinical notes over 13K patients shows that static symptom expansion can successfully expand a set of known symptoms to a disease with high agreement rate with physician input (average precision 0.46), a 31% improvement over baseline co-occurrence based methods. The experimental results also show that the expanded symptoms can serve as useful features for improving AUC measure for disease diagnosis prediction, thus confirming the potential clinical value of our work.