Automatic Extraction of Nested Entities in Clinical Referrals in Spanish

Báez, Pablo; Bravo-Márquez, Felipe; Dunstan, Jocelyn; Rojas, Matias; Villena, Fabián

doi:10.1145/3498324

Cited by 13 publications

(12 citation statements)

References 52 publications

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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%

“…For the computation of word embeddings a training corpus is needed. For this reason, we extracted data from three sources: (1) clinical narratives from The Chilean Waiting List Corpus, which is a collection of diagnostic suspicions from the waiting list in Chilean public hospitals (Báez et al, 2020 ; Báez et al, 2022 ; Villena et al, 2021b ), (2) a medical journal corpus extracted from the SciELO library, which is a collection of articles from several medical journals in Spanish (Villena et al, 2020 ) and (3) a corpus constructed from the Unified Medical Language System (UMLS) (Bodenreider, 2004 ) term graph. We computed embeddings using Word2vec and fastText algorithms and validated them using classic intrinsic evaluation tests adapted to Spanish, such as word pair similarity and semantic textual similarity.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

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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“…An example of an annotation is shown in Figure 2. The result of the annotation process was a file in standoff format [4] , thus including the annotation of tokens with more than one label, which is known as nested entities [21]. In particular, this work was focused on three entities: Diseases, Body Parts, and Medications.…”

Section: Manual Annotation For Nermentioning

confidence: 99%

A transcription and information extraction system to facilitate EHR documentation in Spanish

Fernández

Villena

Rojas

et al. 2023

Preprint

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The large and diverse access to data sources in healthcare has boosted the application of novel computer techniques that can extract meaningful information to improve patients' prognoses and other important medical uses. However, most of the current systems require the professional to type the information in a manual and time-consuming manner, increasing the risk of transcription errors and cross-contamination. One solution is to create an automated system that allows healthcare professionals to dictate clinical information that can be transcribed and analyzed. Since most of the systems proposed so far have been developed for the English language, in this paper, we propose a unified system for automatically recording, transcribing, and identifying key information content from audio in Spanish. Specifically, we propose a two-step model consisting of a commercial Speech-to-Text API and an in-house Named Entity Recognition model trained on Spanish clinical narratives. Transcription performance was evaluated using the Word Error Rate, considering a gold standard composed of 90 manually annotated texts belonging to four domains: general, medical, and dental. Regarding entity detection, we used the F1 score to compare the clinical entities identified by our model with manual annotations performed by healthcare professionals. Finally, to better understand the limitations of our system, we performed a detailed error analysis from a linguistic and computational point of view. We share the annotated referrals, audio, and transcriptions to promote the reproducibility of our results.

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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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Automatic Extraction of Nested Entities in Clinical Referrals in Spanish

Cited by 13 publications

References 52 publications

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

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

A transcription and information extraction system to facilitate EHR documentation in Spanish

Proceedings of the 4th Clinical Natural Language Processing Workshop

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