2012 SympGraphAFrameworkforMiningCli

• (Sondhi et al., 2012) ⇒ Parikshit Sondhi, Jimeng Sun, Hanghang Tong, and ChengXiang Zhai. (2012). “SympGraph: A Framework for Mining Clinical Notes through Symptom Relation Graphs.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339712

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+SympGraph%3A+A+Framework+for+Mining+Clinical+Notes+through+Symptom+Relation+Graphs
• http://dl.acm.org/citation.cfm?id=2339530.2339712&preflayout=flat#citedby

Quotes

Author Keywords

• Information search and retrieval; patient records; physician notes; random walk; symptom graphs

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 SympGraphAFrameworkforMiningCli	ChengXiang Zhai Jimeng Sun Hanghang Tong Parikshit Sondhi			SympGraph: A Framework for Mining Clinical Notes through Symptom Relation Graphs				10.1145/2339530.2339712		2012