Clinical Text Classification in Cancer Real-World Data in Spanish

0Citations
Citations of this article
1Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Healthcare systems currently store a large amount of clinical data, mostly unstructured textual information, such as electronic health records (EHRs). Manually extracting valuable information from these documents is costly for healthcare professionals. For example, when a patient first arrives at an oncology clinical analysis unit, clinical staff must extract information about the type of neoplasm in order to assign the appropriate clinical specialist. Automating this task is equivalent to text classification in natural language processing (NLP). In this study, we have attempted to extract the neoplasm type by processing Spanish clinical documents. A private corpus of 23, 704 real clinical cases has been processed to extract the three most common types of neoplasms in the Spanish territory: breast, lung and colorectal neoplasms. We have developed methodologies based on state-of-the-art text classification task, strategies based on machine learning and bag-of-words, based on embedding models in a supervised task, and based on bidirectional recurrent neural networks with convolutional layers (C-BiRNN). The results obtained show that the application of NLP methods is extremely helpful in performing the task of neoplasm type extraction. In particular, the 2-BiGRU model with convolutional layer and pre-trained fastText embedding obtained the best performance, with a macro-average, more representative than the micro-average due to the unbalanced data, of 0.981 for precision, 0.984 for recall and 0.982 for F1-score.

Cite

CITATION STYLE

APA

Moreno-Barea, F. J., Mesa, H., Ribelles, N., Alba, E., & Jerez, J. M. (2023). Clinical Text Classification in Cancer Real-World Data in Spanish. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13919 LNBI, pp. 482–496). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-34953-9_38

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free