Electronic Health Records Exploitation Using Artificial Intelligence Techniques

Guerra-Tort, Carla; Aguiar-Pulido, Vanessa; Suárez-Ulloa, Victoria; Docampo Boedo, Francisco; López Gestal, José Manuel; Pereira, Javier

Use this link to cite:

http://hdl.handle.net/2183/27154

Electronic Health Records Exploitation Using Artificial Intelligence Techniques

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PereiraLoureiro_Javier_2020_Electronic_Health_Records_Exploitation_Using_Artificial_Intelligence_Techniques.pdf (155.42 KB)

Identifiers

URI: http://hdl.handle.net/2183/27154

Publication date

2020-09-09

Authors

Guerra-Tort, Carla

Aguiar-Pulido, Vanessa

Suárez-Ulloa, Victoria

Docampo Boedo, Francisco

López Gestal, José Manuel

Pereira, Javier

Bibliographic citation

Guerra Tort, C.; Aguiar Pulido, V.; Suárez Ulloa, V.; Docampo Boedo, F.; López Gestal, J. M.; Pereira Loureiro, J. Electronic Health Records Exploitation Using Artificial Intelligence Techniques. Proceedings 2020, 54, 60. https://doi.org/10.3390/proceedings2020054060

Abstract

[Abstract] The exploitation of electronic health records (EHRs) has multiple utilities, from predictive tasks and clinical decision support to pattern recognition. Artificial Intelligence (AI) allows to extract knowledge from EHR data in a practical way. In this study, we aim to construct a Machine Learning model from EHR data to make predictions about patients. Specifically, we will focus our analysis on patients suffering from respiratory problems. Then, we will try to predict whether those patients will have a relapse in less than 6, 12 or 18 months. The main objective is to identify the characteristics that seem to increase the relapse risk. At the same time, we propose an exploratory analysis in search of hidden patterns among data. These patterns will help us to classify patients according to their specific conditions for some clinical variables.