@misc{10481/71910,
year = {2021},
month = {11},
url = {http://hdl.handle.net/10481/71910},
abstract = {In this article, we expose a system developed that extends the Acquired Brain Injury (ABI) diagnostic application known as D-Riska with an artificial intelligence module that supports the diagnosis of ABI enabling therapists to evaluate patients in an assisted way. The application is in charge of collecting the data of the diagnostic tests of the patients, and due to a multi-class Convolutional Neural Network classifier (CNN), it is capable of making predictions that facilitate the diagnosis and the final score obtained in the test by the patient. To find out the best solution to this problem, different classifiers are used to compare the performance of the proposed model based on various classification metrics. The proposed CNN classifier makes predictions with 93 % of Accuracy, 94 % of Precision, 91 %, of Recall and 92% of F1-Score.},
organization = {CRUE-CSIC},
organization = {Springer Nature},
publisher = {Springer},
keywords = {Acquired brain injury},
keywords = {Cognitive test},
keywords = {Deep learning},
keywords = {Convolutional neural networks},
keywords = {D-Riska},
title = {Deep learning assisted cognitive diagnosis for the D-Riska application},
doi = {10.1007/s00500-021-06510-w},
author = {Cuerda, Cristian and Zornoza, Alejandro and Gallud, José A. and Tesoriero, Ricardo and Romero Ayuso, Dulce Nombre de Mari},
}