Automatic Support System for Tumor Coding in Pathology Reports in Spanish

Villena, Fabián; Báez, Pablo; Peñafiel, Sergio; Rojas, Matias; Paredes, Inti; Dunstan, Jocelyn

doi:10.2139/ssrn.3982259

Cited by 5 publications

(2 citation statements)

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“…Our group has experience using static word embeddings for patient classification deployed in a hospital (Villena et al, 2021b ) and using stacked embeddings that combine both static and contextualized embeddings for named entity recognition (Báez et al, 2020 ; Báez et al, 2022 ) and automatic coding (Villena et al, 2021a ). Evaluating the automatic translation of clinical sentences has pointed us the need for creating reliable intrinsic tests created from scratch for the Spanish language, which can be valuable for both static and contextual word embeddings.…”

Section: Discussionmentioning

confidence: 99%

Training and intrinsic evaluation of lightweight word embeddings for the clinical domain in Spanish

Chiu

Villena

Martin

et al. 2022

Front. Artif. Intell.

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Resources for Natural Language Processing (NLP) are less numerous for languages different from English. In the clinical domain, where these resources are vital for obtaining new knowledge about human health and diseases, creating new resources for the Spanish language is imperative. One of the most common approaches in NLP is word embeddings, which are dense vector representations of a word, considering the word's context. This vector representation is usually the first step in various NLP tasks, such as text classification or information extraction. Therefore, in order to enrich Spanish language NLP tools, we built a Spanish clinical corpus from waiting list diagnostic suspicions, a biomedical corpus from medical journals, and term sequences sampled from the Unified Medical Language System (UMLS). These three corpora can be used to compute word embeddings models from scratch using Word2vec and fastText algorithms. Furthermore, to validate the quality of the calculated embeddings, we adapted several evaluation datasets in English, including some tests that have not been used in Spanish to the best of our knowledge. These translations were validated by two bilingual clinicians following an ad hoc validation standard for the translation. Even though contextualized word embeddings nowadays receive enormous attention, their calculation and deployment require specialized hardware and giant training corpora. Our static embeddings can be used in clinical applications with limited computational resources. The validation of the intrinsic test we present here can help groups working on static and contextualized word embeddings. We are releasing the training corpus and the embeddings within this publication1.

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Section: Discussionmentioning

confidence: 99%

Training and intrinsic evaluation of lightweight word embeddings for the clinical domain in Spanish

Chiu

Villena

Martin

et al. 2022

Front. Artif. Intell.

Self Cite

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“…Although the general purpose of this dataset was to be a new resource for named entity recognition, it has also been used to obtain static word embeddings from the clinical domain (Villena et al, 2021b). These representations have boosted the model's performance in several clinical NLP tasks such as tumor encoding (Villena et al, 2021a) and named entity recognition (Báez et al, 2022).…”

Section: Clinical Flairmentioning

confidence: 99%

Proceedings of the 4th Clinical Natural Language Processing Workshop

2022

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The past decade has seen tremendous progress in the field of clinical natural language processing. Driven by new algorithms and access to clinical text from electronic medical records, clinical NLP is quickly becoming a standard tool used in patient care, secondary use and medical research. At the same time, the field of NLP as a whole is undergoing a rapid transformation driven by large language models. Given these developments, it's time that we rethink the future of clinical NLP. Bio: Mark Dredze is the John C Malone Associate Professor of Computer Science at Johns Hopkins University. He is affiliated with the Malone Center for Engineering in Healthcare, the Center for Language and Speech Processing, among others. He holds a secondary appointment in the Biomedical Informatics & Data Science Section (BIDS), under the Department of Medicine (DOM), Division of General Internal Medicine (GIM) in the School of Medicine. He obtained his PhD from the University of Pennsylvania in 2009.Prof. Dredze's research develops statistical models of language with applications to social media analysis, public health and clinical informatics. Within Natural Language Processing he focuses on statistical methods for information extraction but has considered a wide range of NLP tasks, including syntax, semantics, sentiment and spoke language processing. His work in public health includes tobacco control, vaccination, infectious disease surveillance, mental health, drug use, and gun violence prevention. He also develops new methods for clinical NLP on medical records.

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