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Individual patient outcome predictions using supervised learning methods

Published online by Cambridge University Press:  10 May 2018

Abiel Roche-Lima
Affiliation:
University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
Patricia Ordoñez
Affiliation:
University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
Nelson Schwarz
Affiliation:
University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
Adnel Figueroa-Jiménez
Affiliation:
University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
Leonardo A. Garcia-Lebron
Affiliation:
University of Puerto Rico-Medical Sciences Campus, San Juan, Puerto Rico
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Abstract

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OBJECTIVES/SPECIFIC AIMS: To learn the edit distance costs of a symbolic univariate time series representation through a stochastic finite-state transducer to predict patient outcomes in intensive care units. METHODS/STUDY POPULATION: High frequency data of patients in intensive care units were used as a data set. The nearest neighbor method with edit distance costs (learned by the FST) were used to classify the patient status within an hour after 10 hours of data. Several experiments were developed to estimate the parameters that better fit the model regarding the prediction metrics. RESULTS/ANTICIPATED RESULTS: Different metrics were obtained for the several parameters. These metrics were metrics (ie, accuracy, precision, and F-measure). DISCUSSION/SIGNIFICANCE OF IMPACT: Our best results are compared with published works, where most of the metrics (ie, accuracy, precision, and F-measure) were improved.

Type
Biomedical Informatics/Health Informatics
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Association for Clinical and Translational Science 2018