| Title |
Learning stochastic finite-state transducer to predict individual patient outcomes
|
|---|---|
| Published in |
Health and Technology, October 2016
|
| DOI | 10.1007/s12553-016-0146-2 |
| Pubmed ID | |
| Authors |
Patricia Ordoñez, Nelson Schwarz, Adnel Figueroa-Jiménez, Leonardo A. Garcia-Lebron, Abiel Roche-Lima |
| Abstract |
The high frequency data in intensive care unit is flashed on a screen for a few seconds and never used again. However, this data can be used by machine learning and data mining techniques to predict patient outcomes. Learning finite-state transducers (FSTs) have been widely used in problems where sequences need to be manipulated and insertions, deletions and substitutions need to be modeled. In this paper, we learned 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. The Nearest-Neighbor method with these learned costs was used to classify the patient status within an hour after 10 h of data. Several experiments were developed to estimate the parameters that better fit the model regarding the prediction metrics. Our best results are compared with published works, where most of the metrics (i.e., Accuracy, Precision and F-measure) were improved. |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 13 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 4 | 31% |
| Student > Ph. D. Student | 2 | 15% |
| Other | 1 | 8% |
| Professor | 1 | 8% |
| Researcher | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 3 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 23% |
| Medicine and Dentistry | 2 | 15% |
| Biochemistry, Genetics and Molecular Biology | 1 | 8% |
| Engineering | 1 | 8% |
| Unknown | 6 | 46% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 October 2016.
All research outputs
#20,349,664
of 22,896,955 outputs
Outputs from Health and Technology
#214
of 231 outputs
Outputs of similar age
#272,915
of 315,564 outputs
Outputs of similar age from Health and Technology
#6
of 6 outputs
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