scholarly journals Named Entity Recognition Over Electronic Health Records Through a Combined Dictionary-based Approach

2016 ◽  
Vol 100 ◽  
pp. 55-61 ◽  
Author(s):  
Alexandra Pomares Quimbaya ◽  
Alejandro Sierra Múnera ◽  
Rafael Andrés González Rivera ◽  
Julián Camilo Daza Rodríguez ◽  
Oscar Mauricio Muñoz Velandia ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Health Records ◽  
Named Entity ◽  
Electronic Health

scholarly journals De-identifying Spanish medical texts - named entity recognition applied to radiology reports

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Irene Pérez-Díez ◽  
Raúl Pérez-Moraga ◽  
Adolfo López-Cerdán ◽  
Jose-Maria Salinas-Serrano ◽  
María de la Iglesia-Vayá
Keyword(s):  
Electronic Health Records ◽  
English Language ◽  
Personal Information ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Medical Texts ◽  
Health Records ◽  
Named Entity ◽  
Radiology Reports ◽  
Electronic Health

Abstract Background Medical texts such as radiology reports or electronic health records are a powerful source of data for researchers. Anonymization methods must be developed to de-identify documents containing personal information from both patients and medical staff. Although currently there are several anonymization strategies for the English language, they are also language-dependent. Here, we introduce a named entity recognition strategy for Spanish medical texts, translatable to other languages. Results We tested 4 neural networks on our radiology reports dataset, achieving a recall of 97.18% of the identifying entities. Alongside, we developed a randomization algorithm to substitute the detected entities with new ones from the same category, making it virtually impossible to differentiate real data from synthetic data. The three best architectures were tested with the MEDDOCAN challenge dataset of electronic health records as an external test, achieving a recall of 69.18%. Conclusions The strategy proposed, combining named entity recognition tasks with randomization of entities, is suitable for Spanish radiology reports. It does not require a big training corpus, thus it could be easily extended to other languages and medical texts, such as electronic health records.

Concept Attribute Labeling and Context-Aware Named Entity Recognition in Electronic Health Records

2018 ◽  
Vol 7 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Alexandra Pomares-Quimbaya ◽  
Rafael A. Gonzalez ◽  
Oscar Mauricio Muñoz Velandia ◽  
Angel Alberto Garcia Peña ◽  
Julián Camilo Daza Rodríguez ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Ad Hoc ◽  
Named Entity Recognition ◽  
Ensemble Classification ◽  
Entity Recognition ◽  
Classification Model ◽  
Health Records ◽  
Named Entity ◽  
Electronic Health ◽  
Concept Attribute

Extracting valuable knowledge from Electronic Health Records (EHR) represents a challenging task due to the presence of both structured and unstructured data, including codified fields, images and test results. Narrative text in particular contains a variety of notes which are diverse in language and detail, as well as being full of ad hoc terminology, including acronyms and jargon, which is especially challenging in non-English EHR, where there is a dearth of annotated corpora or trained case sets. This paper proposes an approach for NER and concept attribute labeling for EHR that takes into consideration the contextual words around the entity of interest to determine its sense. The approach proposes a composition method of three different NER methods, together with the analysis of the context (neighboring words) using an ensemble classification model. This contributes to disambiguate NER, as well as labeling the concept as confirmed, negated, speculative, pending or antecedent. Results show an improvement of the recall and a limited impact on precision for the NER process.

scholarly journals Named Entity Recognition for Clinical Portuguese Corpus with Conditional Random Fields and Semantic Groups

2019 ◽  
Author(s):  
João Vitor Andrioli De Souza ◽  
Yohan Bonescki Gumiel ◽  
Lucas Emanuel Silva e Oliveira ◽  
Claudia Maria Cabral Moro
Keyword(s):  
Electronic Health Records ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Health Records ◽  
Named Entity ◽  
Electronic Health

Considering the difficulties of extracting entities from Electronic Health Records (EHR) texts in Portuguese, we explore the Conditional Random Fields (CRF) algorithm to build a Named Entity Recognition (NER) system based on a corpus of clinical Portuguese data annotated by experts. We acquaint the challenges and methods to classify Abbreviations, Disorders, Procedures and Chemicals within the texts. By selecting a meaningful set of features, and parameters with the best performance the results demonstrate that the method is promising and may support other biomedical tasks, nonetheless, further experiments with more features, different architectures and sophisticated preprocessing steps are needed.

scholarly journals Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records

2021 ◽  
Vol 13 (5) ◽  
pp. 136
Author(s):  
Claudia Alessandra Libbi ◽  
Jan Trienes ◽  
Dolf Trieschnigg ◽  
Christin Seifert
Keyword(s):  
Electronic Health Records ◽  
Named Entity Recognition ◽  
Synthetic Data ◽  
Real Data ◽  
Entity Recognition ◽  
Language Models ◽  
Text Generation ◽  
Health Records ◽  
Named Entity ◽  
Electronic Health

A major hurdle in the development of natural language processing (NLP) methods for Electronic Health Records (EHRs) is the lack of large, annotated datasets. Privacy concerns prevent the distribution of EHRs, and the annotation of data is known to be costly and cumbersome. Synthetic data presents a promising solution to the privacy concern, if synthetic data has comparable utility to real data and if it preserves the privacy of patients. However, the generation of synthetic text alone is not useful for NLP because of the lack of annotations. In this work, we propose the use of neural language models (LSTM and GPT-2) for generating artificial EHR text jointly with annotations for named-entity recognition. Our experiments show that artificial documents can be used to train a supervised named-entity recognition model for de-identification, which outperforms a state-of-the-art rule-based baseline. Moreover, we show that combining real data with synthetic data improves the recall of the method, without manual annotation effort. We conduct a user study to gain insights on the privacy of artificial text. We highlight privacy risks associated with language models to inform future research on privacy-preserving automated text generation and metrics for evaluating privacy-preservation during text generation.

Concept Attribute Labeling and Context-Aware Named Entity Recognition in Electronic Health Records

2020 ◽  
pp. 325-339
Author(s):  
Alexandra Pomares-Quimbaya ◽  
Rafael A. Gonzalez ◽  
Oscar Mauricio Muñoz Velandia ◽  
Angel Alberto Garcia Peña ◽  
Julián Camilo Daza Rodríguez ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Ad Hoc ◽  
Named Entity Recognition ◽  
Ensemble Classification ◽  
Entity Recognition ◽  
Classification Model ◽  
Health Records ◽  
Named Entity ◽  
Electronic Health ◽  
Concept Attribute

Extracting valuable knowledge from Electronic Health Records (EHR) represents a challenging task due to the presence of both structured and unstructured data, including codified fields, images and test results. Narrative text in particular contains a variety of notes which are diverse in language and detail, as well as being full of ad hoc terminology, including acronyms and jargon, which is especially challenging in non-English EHR, where there is a dearth of annotated corpora or trained case sets. This paper proposes an approach for NER and concept attribute labeling for EHR that takes into consideration the contextual words around the entity of interest to determine its sense. The approach proposes a composition method of three different NER methods, together with the analysis of the context (neighboring words) using an ensemble classification model. This contributes to disambiguate NER, as well as labeling the concept as confirmed, negated, speculative, pending or antecedent. Results show an improvement of the recall and a limited impact on precision for the NER process.

scholarly journals Recent advances in Swedish and Spanish medical entity recognition in clinical texts using deep neural approaches

2019 ◽  
Vol 19 (S7) ◽  
Author(s):  
Rebecka Weegar ◽  
Alicia Pérez ◽  
Arantza Casillas ◽  
Maite Oronoz
Keyword(s):  
Electronic Health Records ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Learning Methods ◽  
Health Records ◽  
Clinical Text ◽  
Named Entity ◽  
Text Corpora ◽  
Electronic Health

Abstract Background Text mining and natural language processing of clinical text, such as notes from electronic health records, requires specific consideration of the specialized characteristics of these texts. Deep learning methods could potentially mitigate domain specific challenges such as limited access to in-domain tools and data sets. Methods A bi-directional Long Short-Term Memory network is applied to clinical notes in Spanish and Swedish for the task of medical named entity recognition. Several types of embeddings, both generated from in-domain and out-of-domain text corpora, and a number of generation and combination strategies for embeddings have been evaluated in order to investigate different input representations and the influence of domain on the final results. Results For Spanish, a micro averaged F1-score of 75.25 was obtained and for Swedish, the corresponding score was 76.04. The best results for both languages were achieved using embeddings generated from in-domain corpora extracted from electronic health records, but embeddings generated from related domains were also found to be beneficial. Conclusions A recurrent neural network with in-domain embeddings improved the medical named entity recognition compared to shallow learning methods, showing this combination to be suitable for entity recognition in clinical text for both languages.

scholarly journals De-identifying Spanish medical texts - Named Entity Recognition applied to radiology reports

2020 ◽  
Author(s):  
Irene Pérez-Díez ◽  
Raúl Pérez-Moraga ◽  
Adolfo López-Cerdán ◽  
Jose-Maria Salinas-Serrano ◽  
María de la Iglesia-Vayá
Keyword(s):  
Electronic Health Records ◽  
English Language ◽  
Personal Information ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Medical Texts ◽  
Health Records ◽  
Named Entity ◽  
Radiology Reports ◽  
Electronic Health

Medical texts such as radiology reports or electronic health records are a powerful source of data for researchers. Anonymization methods must be developed to de-identify documents containing personal information from both patients and medical staff. Although currently there are several anonymization strategies for the English language, they are also language-dependent. Here, we introduce a named entity recognition strategy for Spanish medical texts, translatable to other languages. We tested 4 neural networks on our radiology reports dataset, achieving a recall of 97.18% of the identifying entities. Along-side, we developed a randomization algorithm to substitute the detected entities with new ones from the same category, making it virtually impossible to differentiate real data from synthetic data. The three best architectures were tested with the MEDDOCAN challenge dataset of electronic health records as an external test, achieving a recall of 69.18%. The strategy proposed, combining named entity recognition tasks with randomization of entities, is suitable for Spanish radiology reports. It does not require a big training corpus, thus it can be easily extended to other languages and medical texts, such as electronic health records.

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