NUBES: A Corpus of Negation and Uncertainty in Spanish Clinical Texts

Lima, Salvador Vilar Correia; Pérez, Naiara; Cuadros, Montse; Rigau, Germán

doi:10.48550/arxiv.2004.01092

Cited by 1 publication

(6 citation statements)

References 12 publications

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“…In addition, speculation detection has not yet been fully addressed for Spanish clinical narratives. We found only the [12] proposal dealing with this issue. Obtained results in our approach show similar performance rates than those reported by [12].…”

Section: Named Entity Recognition Resultsmentioning

confidence: 99%

“…We found only the [12] proposal dealing with this issue. Obtained results in our approach show similar performance rates than those reported by [12]. The impact of negation and speculation to extract correctly the diagnosis date will be evaluated as it is shown in the planned experiments:…”

Section: Named Entity Recognition Resultsmentioning

confidence: 99%

“…Speculation is a linguistic phenomenon that frequently appears in clinical notes, and is inherent in many medical decisions [11]. Moreover, in the medical domain, some authors also refer to speculation as uncertainty [12,13]. Physicians often face uncertain findings when diagnosing or treating patients.…”

Section: Introductionmentioning

confidence: 99%

“…org/?hl=es-419) and Keras (https://keras.io/) using the following parameters: learning rate as 0.001, dropout as 0.5, the number of epochs is 30, the BiLSTM hidden size is 300, and the batch size is 512. • To validate the negation and speculation detection step, the NUBES corpus proposed by [12] was used. This public corpus is annotated with speculation and negation in clinical notes written in Spanish.…”

mentioning

confidence: 99%

“…The resulting lexicon offers high precision in the cue detection task. • Some weaknesses reported in [12], such as post-scope recognition are addressed in this proposal by using Regex 2 (see Section 5.2). When a cue is detected using this Regex, the scope is searched to the left of the cue.…”

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confidence: 99%

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Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes

et al. 2021

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Despite efforts to develop models for extracting medical concepts from clinical notes, there are still some challenges in particular to be able to relate concepts to dates. The high number of clinical notes written for each single patient, the use of negation, speculation, and different date formats cause ambiguity that has to be solved to reconstruct the patient’s natural history. In this paper, we concentrate on extracting from clinical narratives the cancer diagnosis and relating it to the diagnosis date. To address this challenge, a hybrid approach that combines deep learning-based and rule-based methods is proposed. The approach integrates three steps: (i) lung cancer named entity recognition, (ii) negation and speculation detection, and (iii) relating the cancer diagnosis to a valid date. In particular, we apply the proposed approach to extract the lung cancer diagnosis and its diagnosis date from clinical narratives written in Spanish. Results obtained show an F-score of 90% in the named entity recognition task, and a 89% F-score in the task of relating the cancer diagnosis to the diagnosis date. Our findings suggest that speculation detection is together with negation detection a key component to properly extract cancer diagnosis from clinical notes.

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Section: Named Entity Recognition Resultsmentioning

confidence: 99%

Section: Named Entity Recognition Resultsmentioning

confidence: 99%