2015 DynamicallyModelingPatientsHeal

• (Caballero Barajas & Akella, 2015) ⇒ Karla L. Caballero Barajas, and Ram Akella. (2015). “Dynamically Modeling Patient's Health State from Electronic Medical Records: A Time Series Approach.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783289

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Dynamically+Modeling+Patient%27s+Health+State+from+Electronic+Medical+Records%3A+A+Time+Series+Approach
• http://dl.acm.org/citation.cfm?id=2783258.2783289&preflayout=flat#citedby

Quotes

Author Keywords

• Dynamic linear models; health; mortality prediction; statistical topic models; text mining; time series analysis

Abstract

In this paper, we present a method to dynamically estimate the probability of mortality inside the Intensive Care Unit (ICU) by combining heterogeneous data. We propose a method based on Generalized Linear Dynamic Models that models the probability of mortality as a latent state that evolves over time. This framework allows us to combine different types of features (lab results, vital signs readings, doctor and nurse notes, etc) into a single state, which is updated each time new patient data is observed. In addition, we include the use of text features, based on medical noun phrase extraction and Statistical Topic Models. These features provide context about the patient that cannot be captured when only numerical features are used. We fill out the missing values using a Regularized Expectation Maximization based method assuming temporal data. We test our proposed approach using 15,000 Electronic Medical Records (EMRs) obtained from the MIMIC II public dataset. Experimental results show that the proposed model allows us to detect an increase in the probability of mortality before it occurs. We report an AUC 0.8657. Our proposed model clearly outperforms other methods of the literature in terms of sensitivity with 0.7885 compared to 0.6559 of Naive Bayes and F-score with 0.5929 compared to 0.4662 of Apache III score after 24 hours.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 DynamicallyModelingPatientsHeal	Karla L. Caballero Barajas Ram Akella			Dynamically Modeling Patient's Health State from Electronic Medical Records: A Time Series Approach				10.1145/2783258.2783289		2015