2015 TemporalPhenotypingfromLongitud

• (Liu et al., 2015) ⇒ Chuanren Liu, Fei Wang, Jianying Hu, and Hui Xiong. (2015). “Temporal Phenotyping from Longitudinal Electronic Health Records: A Graph Based Framework.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783352

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Cited By

• http://scholar.google.com/scholar?q=%222015%22+Temporal+Phenotyping+from+Longitudinal+Electronic+Health+Records%3A+A+Graph+Based+Framework
• http://dl.acm.org/citation.cfm?id=2783258.2783352&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; electronic health records; regularization; temporal graph; temporal phenotyping

Abstract

The rapid growth in the development of healthcare information systems has led to an increased interest in utilizing the patient Electronic Health Records (EHR) for assisting disease diagnosis and phenotyping. The patient EHRs are generally longitudinal and naturally represented as medical event sequences, where the events include clinical notes, problems, medications, vital signs, laboratory reports, etc. The longitudinal and heterogeneous properties make EHR analysis an inherently difficult challenge. To address this challenge, in this paper, we develop a novel representation, namely the temporal graph, for such event sequences. The temporal graph is informative for a variety of challenging analytic tasks, such as predictive modeling, since it can capture temporal relationships of the medical events in each event sequence. By summarizing the longitudinal data, the temporal graphs are also robust and resistant to noisy and irregular observations. Based on the temporal graph representation, we further develop an approach for temporal phenotyping to identify the most significant and interpretable graph basis as phenotypes. This helps us better understand the disease evolving patterns. Moreover, by expressing the temporal graphs with the phenotypes, the expressing coefficients can be used for applications such as personalized medicine, disease diagnosis, and patient segmentation. Our temporal phenotyping framework is also flexible to incorporate semi-supervised / supervised information. Finally, we validate our framework on two real-world tasks. One is predicting the onset risk of heart failure. Another is predicting the risk of heart failure related hospitalization for patients with COPD pre-condition. Our results show that the diagnosis performance in both tasks can be improved significantly by the proposed approaches. Also, we illustrate some interesting phenotypes derived from the data.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 TemporalPhenotypingfromLongitud	Hui Xiong Jianying Hu Fei Wang Chuanren Liu			Temporal Phenotyping from Longitudinal Electronic Health Records: A Graph Based Framework				10.1145/2783258.2783352		2015