RT Journal Article
T1 Artificial intelligence to assist specialists in the detection of haematological diseases
A1 Díaz-del-Pino, Sergio
A1 Trelles-Martínez, Roberto
A1 González-Fernández, F.A.
A1 Guil-Mata, Nicolás
K1 Inteligencia artificial
K1 Hematología
AB Artificial intelligence, particularly the growth of neural network research and development, has become an invaluable tool for data analysis, offering unrivalled solutions for image generation, natural language processing, and personalised suggestions. In the meantime, biomedicine has been presented as one of the pressing challenges of the 21st century. The inversion of the agepyramid, the increase in longevity, and the negative environment due to pollution and bad habits of the population have led to a necessity of research in the methodologies that can help to mitigate and fight against these changes.The combination of both fields has already achieved remarkable results in drug discovery, cancer prediction or gene activation. However, challenges such as data labelling, architecture improvements, interpretability of the models and translational implementation of the proposals still remain. In haematology, conventional protocols follow a stepwise approach that includesseveral tests and doctor-patient interactions to make a diagnosis. This procedure results in significant costs and workload for hospitals.In this paper, we present an artificial intelligence model based on neural networks to support practitioners in the identification of different haematological diseases using only rutinary and inexpensive blood count tests. In particular, we present both binary and multiclass classification of haematological diseases using a specialised neural network architecture where data is studiedand combined along it, taking into account the clinical knowledge of the problem, obtaining results up to 96% accuracy for the binary classification experiment. Furthermore, we compare this method against traditional machine learning algorithms such as gradient boosting decision trees and transformers for tabular data.....
PB Elsevier
YR 2023
FD 2023
LK https://hdl.handle.net/10630/26931
UL https://hdl.handle.net/10630/26931
LA eng
NO Diaz-del-Pino, S., Trelles-Martinez, R., González-Fernández, F. A., & Guil, N. (2023). Artificial intelligence to assist specialists in the detection of haematological diseases. Heliyon, 9(5), e15940–e15940. https://doi.org/10.1016/j.heliyon.2023.e15940
NO This work has been partially supported by the European project (grant no. 676559) (European Union), the Spanish national projectPlataforma de Recursos Biomoleculares y Bioinformáticos (ISCIII-PT13.0001.0012 and ISCIII-PT17.0009.0022) (Spain), the FondoEuropeo de Desarrollo Regional (UMA18-FEDERJA-156, UMA20-FEDERJA-059) (Andalucia, Spain), the Junta de Andalucía (P18-FR-3130) (Andalucia, Spain), the Instituto de Investigación Biomédica de Málaga IBIMA and the University of Málaga (Spain)Partial funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 4 mar 2026