Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach

Pabón, Oswaldo Solarte; Montenegro, Orlando; Torrente, María; González, Alejandro Rodríguez; Provencio, Mariano; Ernestina, Menasalvas

doi:10.7717/peerj-cs.913

Cited by 23 publications

(20 citation statements)

References 75 publications

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“…Enhanced predictions may offer potential advantages, including facilitating treatment planning, guiding population management efforts, and fostering discussions about goals of care [ 14 , 23 ]. Predictive analytics may aid the delivery of oncology treatments to populations that are disadvantaged or underrepresented in clinical trials for whom it is challenging to apply evidence-based medicine [ 24 ]. For example, among older adults, AI predictive analytics may help oncologists anticipate problems that have not been reported previously or identify risk factors for chemotherapy toxicity that are overlooked in daily practice given that these patients are usually not eligible for clinical trials.…”

Section: Discussionmentioning

confidence: 99%

An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study

Torrente

Sousa

Hernández

et al. 2022

Cancers

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Background: Artificial intelligence (AI) has contributed substantially in recent years to the resolution of different biomedical problems, including cancer. However, AI tools with significant and widespread impact in oncology remain scarce. The goal of this study is to present an AI-based solution tool for cancer patients data analysis that assists clinicians in identifying the clinical factors associated with poor prognosis, relapse and survival, and to develop a prognostic model that stratifies patients by risk. Materials and Methods: We used clinical data from 5275 patients diagnosed with non-small cell lung cancer, breast cancer, and non-Hodgkin lymphoma at Hospital Universitario Puerta de Hierro-Majadahonda. Accessible clinical parameters measured with a wearable device and quality of life questionnaires data were also collected. Results: Using an AI-tool, data from 5275 cancer patients were analyzed, integrating clinical data, questionnaires data, and data collected from wearable devices. Descriptive analyses were performed in order to explore the patients’ characteristics, survival probabilities were calculated, and a prognostic model identified low and high-risk profile patients. Conclusion: Overall, the reconstruction of the population’s risk profile for the cancer-specific predictive model was achieved and proved useful in clinical practice using artificial intelligence. It has potential application in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.

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Section: Discussionmentioning

confidence: 99%

An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study

Torrente

Sousa

Hernández

et al. 2022

Cancers

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“…Finding the absence of lesions or tumor recurrence is crucial for determining cancer diagnosis. There are several patterns for negation clues to describe the absence in colonoscopy reports [ 57 ]. For example, "There was no evidence of tumor recurrence.…”

Section: Methodsmentioning

confidence: 99%

Deep learning approach to detection of colonoscopic information from unstructured reports

Seong

Choi

Shin

et al. 2023

BMC Med Inform Decis Mak

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Background Colorectal cancer is a leading cause of cancer deaths. Several screening tests, such as colonoscopy, can be used to find polyps or colorectal cancer. Colonoscopy reports are often written in unstructured narrative text. The information embedded in the reports can be used for various purposes, including colorectal cancer risk prediction, follow-up recommendation, and quality measurement. However, the availability and accessibility of unstructured text data are still insufficient despite the large amounts of accumulated data. We aimed to develop and apply deep learning-based natural language processing (NLP) methods to detect colonoscopic information. Methods This study applied several deep learning-based NLP models to colonoscopy reports. Approximately 280,668 colonoscopy reports were extracted from the clinical data warehouse of Samsung Medical Center. For 5,000 reports, procedural information and colonoscopic findings were manually annotated with 17 labels. We compared the long short-term memory (LSTM) and BioBERT model to select the one with the best performance for colonoscopy reports, which was the bidirectional LSTM with conditional random fields. Then, we applied pre-trained word embedding using large unlabeled data (280,668 reports) to the selected model. Results The NLP model with pre-trained word embedding performed better for most labels than the model with one-hot encoding. The F1 scores for colonoscopic findings were: 0.9564 for lesions, 0.9722 for locations, 0.9809 for shapes, 0.9720 for colors, 0.9862 for sizes, and 0.9717 for numbers. Conclusions This study applied deep learning-based clinical NLP models to extract meaningful information from colonoscopy reports. The method in this study achieved promising results that demonstrate it can be applied to various practical purposes.

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“…• Malayalam: generation of synoptic clinical reports [39]; • Polish: prediction of cardiovascular diseases in electronic health records [40]; • (Brazilian) Portuguese: description of an annotated clinical corpus [41], ICD-10 coding [42]; • Serbian: sentiment analysis in COVID-19 tweets [43]; • Spanish: ICD-coding [10, 44], negation and uncertainty detection in clinical narratives [45], training and evaluation of word embeddings for the clinical domain [46]; • Swedish: ICD-10 coding [44];…”

Section: Languages Addressedmentioning

confidence: 99%

Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area

Grouin,

Grabar

2023

Yearb Med Inform

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Objectives: To analyse the content of publications within the medical Natural Language Processing (NLP) domain in 2022. Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues. Results: Three best papers have been selected. We also propose an analysis of the content of the NLP publications in 2022, stressing on some of the topics. Conclusion: The main trend in 2022 is certainly related to the availability of large language models, especially those based on Transformers, and to their use by non-NLP researchers. This leads to the democratization of the NLP methods. We also observe the renewal of interest to languages other than English, the continuation of research on information extraction and prediction, the massive use of data from social media, and the consideration of needs and interests of patients.

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Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach

Cited by 23 publications

References 75 publications

An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study

An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study

Deep learning approach to detection of colonoscopic information from unstructured reports

Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area

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