Use this link to cite:

http://hdl.handle.net/2183/28957

Training of machine learning models for recurrence prediction in patients with respiratory pathologies

Thumbnail Image

Files

MolineroRguez_Training.pdf (191.86 KB)

Identifiers

URI: http://hdl.handle.net/2183/28957
DOI: 10.3390/engproc2021007020

Publication date

2021-10-13

Authors

Molinero-Rodríguez, Ainhoa
Guerra-Tort, Carla
Suárez-Ulloa, Victoria
López Gestal, José Manuel

Advisors

Other responsabilities

Journal Title

Bibliographic citation

Molinero Rodríguez A, Guerra Tor C, Suárez Ulloa V, López Gestal JM, Pereira J, Aguiar Pulido V. Training of machine learning models for recurrence prediction in patients with respiratory pathologies. Eng Proc. 2021;7(1):20.

Type of academic work

Academic degree

Abstract

[Abstract] Information extracted from electronic health records (EHRs) is used for predictive tasks and clinical pattern recognition. Machine learning techniques also allow the extraction of knowledge from EHR. This study is a continuation of previous work in which EHRs were exploited to make predictions about patients with respiratory diseases. In this study, we will try to predict the recurrence of patients with respiratory diseases using four different machine learning algorithms.

Description

Proceeding paper

Keywords

Electronic health record (EHR) Machine learning Linear discriminant analysis Quadratic discriminant analysis K-nearest neighbors Decision trees

Editor version

https://doi.org/10.3390/engproc2021007020

Rights

Creative Commons Attribution 4.0 International License (CC-BY 4.0)

Collections

Investigación (FCS)
Creative Commons Attribution 4.0 International License (CC-BY 4.0)

Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International License (CC-BY 4.0)