Construcción de recursos de texto para la identificación automática de información clínica en narrativas no estructuradas

Báez, Pablo; Villena, Fabián; Zúñiga, Karen; Jones, Natalia R.; Fernández, Gustavo; Durán, Manuel; Dunstan, Jocelyn

doi:10.4067/s0034-98872021000701014

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…For clinical language, common choices of tags are diseases, medications, or body parts. In the case of Chile, in 2022 it was released the Chilean Waiting List Corpus 6 consisted of 9,000 primary care referrals annotated with ten entities, six attributes, and pairs of relations with clinical relevance [19,20]. This corpus was used to train the automatic recognition of clinical entities, with the results summarized in Baez et al [21].…”

Section: Corpus Annotation For the Automatic Detection Of Clinical En...mentioning

confidence: 99%

A pseudonymized corpus of occupational health narratives for clinical entity recognition in Spanish

Dunstan,

Vakili,

Miranda

et al. 2024

Preprint

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Despite the high creation cost, annotated corpora are indispensable for robust natural language processing systems. In the clinical field, apart from annotating medical entities, personally identifiable information (PII) must be removed, especially in the era of large language models where unwanted memorization can occur. This paper presents a corpus annotated to anonymize personally identifiable information in 1,787 anamneses of work-related accidents and diseases in Spanish. In addition, we applied a previously released model for Named Entity Recognition (NER) trained on referrals from primary care physicians to identify diseases, body parts, and medications in this work-related text. We analyzed the differences between the models and the gold standard curated by a physician in detail. Moreover, we compared the performance of the NER model on the original narratives, in narratives where personal information has been masked, and in texts where the personal data is replaced by another similar expression (pseudonymization). Within this publication, we share the annotation guidelines and the annotated corpus.

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Section: Corpus Annotation For the Automatic Detection Of Clinical En...mentioning

confidence: 99%

A pseudonymized corpus of occupational health narratives for clinical entity recognition in Spanish

Dunstan,

Vakili,

Miranda

et al. 2024

Preprint

Self Cite

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“…Several authors have reported the processing of mammographic reports using NLP tools in the scientific literature using a supervised process. Báez et al 11 describe a method for building medical class resources and the labeling process for a supervised approach in clinical reports with unstructured narratives. Bozkurt et al 12 presented an NLP information extraction pipeline in the General Architecture for Text Engineering (GATE) NLP toolkit, where sequential processing modules were executed, producing an output information frame required for a mammography decision support system.…”

Section: Related Workmentioning

confidence: 99%

A named entity recognition framework using transformers to identify relevant clinical findings from mammographic radiological reports

Godoy

Chabert

Querales

et al. 2023

18th International Symposium on Medical Information Processing and Analysis

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Detecting and extracting findings in a radiological report is crucial for text mining tasks in several applications. In this case, a labeled process for the image associated with the radiological report in mammography and Spanish context for a computer vision model is required. This paper shows the methodology and process generated for this goal. This paper presents a Named Entity Recognition (NER) approach based on a transformer deep learning model, using a labeled corpus and fine-tuning process to find three concepts that compose a typical finding in a mammographic radiological report: laterality, location, and the finding. We add another concept in the labeled process, the negation, necessary to identify falses positive inside the text that writes the radiologist. Our model achieves an F1 score of 88.24% classifying the three principal concepts for a finding, product of the labeled and fine-tuning process. The results presented here will be used as input for future training work on a computer vision model.

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“…The manual annotation consists of humans detecting pre-defined pieces of information declared in the annotation guidelines and assigning the corresponding labels. In order to assure the quality of this process, more than one person annotates, and intra/inter agreement is tracked [24,25]. In this section, only used medical and dental texts were annotated with clinical entities since general-domain texts were used only to compare the transcription quality between general and clinical domains.…”

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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Construcción de recursos de texto para la identificación automática de información clínica en narrativas no estructuradas

Cited by 6 publications

References 20 publications

A pseudonymized corpus of occupational health narratives for clinical entity recognition in Spanish

A pseudonymized corpus of occupational health narratives for clinical entity recognition in Spanish

A named entity recognition framework using transformers to identify relevant clinical findings from mammographic radiological reports

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

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