2014 MarbleHighThroughputPhenotyping

• (Ho et al., 2014) ⇒ Joyce C. Ho, Joydeep Ghosh, and Jimeng Sun. (2014). “Marble: High-throughput Phenotyping from Electronic Health Records via Sparse Nonnegative Tensor Factorization.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623658

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Marble%3A+High-throughput+Phenotyping+from+Electronic+Health+Records+via+Sparse+Nonnegative+Tensor+Factorization
• http://dl.acm.org/citation.cfm?id=2623330.2623658&preflayout=flat#citedby

Quotes

Author Keywords

• Application; data mining; dimensionality reduction; ehr phenotyping; tensor

Abstract

The rapidly increasing availability of electronic health records (EHRs) from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical research, decision making, prognosis, and patient management. Unfortunately, EHR data do not always directly and reliably map to phenotypes, or medical concepts, that clinical researchers need or use. Existing phenotyping approaches typically require labor intensive supervision from medical experts. We propose Marble, a novel sparse non-negative tensor factorization method to derive phenotype candidates with virtually no human supervision. Marble decomposes the observed tensor into two terms, a bias tensor and an interaction tensor. The bias tensor represents the baseline characteristics common amongst the overall population and the interaction tensor defines the phenotypes. We demonstrate the capability of our proposed model on both simulated and patient data from a publicly available clinical database. Our results show that Marble derived phenotypes provide at least a 42.8% reduction in the number of non-zero element and also retains predictive power for classification purposes. Furthermore, the resulting phenotypes and baseline characteristics from real EHR data are consistent with known characteristics of the patient population. Thus it can potentially be used to rapidly characterize, predict, and manage a large number of diseases, thereby promising a novel, data-driven solution that can benefit very large segments of the population.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 MarbleHighThroughputPhenotyping	Joydeep Ghosh Jimeng Sun Joyce C. Ho			Marble: High-throughput Phenotyping from Electronic Health Records via Sparse Nonnegative Tensor Factorization				10.1145/2623330.2623658		2014