2026-05-28T22:46:43Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/273522026-02-03T12:06:48Zcom_10630_2254col_10630_37959
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Moreno-Barea, Francisco J.
author
Mesa, Héctor
author
Ribelles, Nuria
author
Alba-Conejo, Emilio
author
Jerez-Aragonés, José Manuel
2023-07-24T09:19:01Z
2023-07-24T09:19:01Z
2023-06-29
Moreno-Barea, F.J. et al. (2023). Clinical Text Classification in Cancer Real-World Data in Spanish. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13919. Springer, Cham. https://doi.org/10.1007/978-3-031-34953-9_38
https://hdl.handle.net/10630/27352
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.
eng
Clinical text classification in Cancer Real-World Data in Spanish
conference output
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URL
https://riuma.uma.es/bitstreams/d432862a-b2a7-48d6-8af0-31afac5fcca2/download
File
MD5
0546a60c407b9d030f439fac06567c94
281300
application/pdf
Moreno-Barea et al. 2023 - Clinical Text Classification in Cancer Real-World Data in Spanish. In Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13919. Spriger, Cham.pdf