García Terriza, L.; Risco-Martín, J.; Ayala, J. and Roselló, G. BIOINFORMATICS 2023, ISBN 978-989-758-631-6; ISSN 2184-4305, pages 131-138. DOI: 10.5220/0011621000003414
https://www.scitepress.org/Papers/2023/116210/116210.pdf
Abstract: This work presents an integrated recommendation system capable of providing support in healthcare critical environments such as Intensive Care Units or Stroke Care Units using Machine Learning techniques. The system can manage several patients by reading monitoring hemodynamic data in real-time, presenting current death risk probability, and showing recommendations that would reduce such probability and, in some cases, avoid death. This system introduces a novel method to produce recommendations based on genetic models and supervised machine learning. The interface is built upon a web application where clinicians can evaluate recommendations and straightforwardly provide feedback.
Funding: This research has been funded by Instituto de Salud Carlos III (RICORS-RD21/0006/0009) and cofinanced with FEDER Funds and/or from the European funds of the Recovery, Transformation and Resilience Plan and by NextGenerationEU. This work is also supported by Spanish Ministry of Science and Innovation under project PID2019-110866RB-I00.