scholarly journals Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods

2018 ◽  
Vol 6 (4) ◽  
pp. e50 ◽  
Author(s):  
Yu Zhang ◽  
Xuwen Wang ◽  
Zhen Hou ◽  
Jiao Li
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Learning Methods ◽  
Health Records ◽  
Named Entity ◽  
Machine Learning Methods ◽  
Electronic Health

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 Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods (Preprint)

2018 ◽  
Author(s):  
Yu Zhang ◽  
Xuwen Wang ◽  
Zhen Hou ◽  
Jiao Li
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Patient Treatment ◽  
Free Text ◽  
Health Records ◽  
Named Entity ◽  
Bidirectional Lstm ◽  
Electronic Health

BACKGROUND Electronic health records (EHRs) are important data resources for clinical studies and applications. Physicians or clinicians describe patients’ disorders or treatment procedures in EHRs using free text (unstructured) clinical notes. The narrative information plays an important role in patient treatment and clinical research. However, it is challenging to make machines understand the clinical narratives. OBJECTIVE This study aimed to automatically identify Chinese clinical entities from free text in EHRs and make machines semantically understand diagnoses, tests, body parts, symptoms, treatments, and so on. METHODS The dataset we used for this study is the benchmark dataset with human annotated Chinese EHRs, released by the China Conference on Knowledge Graph and Semantic Computing 2017 clinical named entity recognition challenge task. Overall, 2 machine learning models, the conditional random fields (CRF) method and bidirectional long short-term memory (LSTM)-CRF, were applied to recognize clinical entities from Chinese EHR data. To train the CRF–based model, we selected features such as bag of Chinese characters, part-of-speech tags, character types, and the position of characters. For the bidirectional LSTM-CRF–based model, character embeddings and segmentation information were used as features. In addition, we also employed a dictionary-based approach as the baseline for the purpose of performance evaluation. Precision, recall, and the harmonic average of precision and recall (F1 score) were used to evaluate the performance of the methods. RESULTS Experiments on the test set showed that our methods were able to automatically identify types of Chinese clinical entities such as diagnosis, test, symptom, body part, and treatment simultaneously. With regard to overall performance, CRF and bidirectional LSTM-CRF achieved a precision of 0.9203 and 0.9112, recall of 0.8709 and 0.8974, and F1 score of 0.8949 and 0.9043, respectively. The results also indicated that our methods performed well in recognizing each type of clinical entity, in which the “symptom” type achieved the best F1 score of over 0.96. Moreover, as the number of features increased, the F1 score of the CRF model increased from 0.8547 to 0.8949. CONCLUSIONS In this study, we employed two computational methods to simultaneously identify types of Chinese clinical entities from free text in EHRs. With training, these methods can effectively identify various types of clinical entities (eg, symptom and treatment) with high accuracy. The deep learning model, bidirectional LSTM-CRF, can achieve better performance than the CRF model with little feature engineering. This study contributed to translating human-readable health information into machine-readable information.

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 Machine Learning Methods to Extract Documentation of Breast Cancer Symptoms From Electronic Health Records

2018 ◽  
Vol 55 (6) ◽  
pp. 1492-1499 ◽  
Author(s):  
Alexander W. Forsyth ◽  
Regina Barzilay ◽  
Kevin S. Hughes ◽  
Dickson Lui ◽  
Karl A. Lorenz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Electronic Health Records ◽  
Learning Methods ◽  
Health Records ◽  
Cancer Symptoms ◽  
Machine Learning Methods ◽  
Electronic Health

scholarly journals Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Matthew D. Nemesure ◽  
Michael V. Heinz ◽  
Raphael Huang ◽  
Nicholas C. Jacobson
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Undergraduate Students ◽  
General Health ◽  
Predictive Performance ◽  
Future Research ◽  
Learning Methods ◽  
Health Records ◽  
Machine Learning Methods ◽  
Electronic Health

AbstractGeneralized anxiety disorder (GAD) and major depressive disorder (MDD) are highly prevalent and impairing problems, but frequently go undetected, leading to substantial treatment delays. Electronic health records (EHRs) collect a great deal of biometric markers and patient characteristics that could foster the detection of GAD and MDD in primary care settings. We approached the problem of predicting MDD and GAD using a novel machine learning pipeline to re-analyze data from an observational study. The pipeline constitutes an ensemble of algorithmically distinct machine learning methods, including deep learning. A sample of 4,184 undergraduate students completed the study, undergoing a general health screening and completing a psychiatric assessment for MDD and GAD. After explicitly excluding all psychiatric information, 59 biomedical and demographic features from the general health survey in addition to a set of engineered features were used for model training. We assessed the model's performance on a held-out test set and found an AUC of 0.73 (sensitivity: 0.66, specificity: 0.7) and 0.67 (sensitivity: 0.55, specificity: 0.7) for GAD, and MDD, respectively. Additionally, we used advanced techniques (SHAP values) to illuminate which features had the greatest impact on prediction for each disease. The top predictive features for MDD were being satisfied with living conditions and having public health insurance. The top predictive features for GAD were vaccinations being up to date and marijuana use. Our results indicate moderate predictive performance for the application of machine learning methods in detection of GAD and MDD based on EHR data. By identifying important predictors of GAD and MDD, these results may be used in future research to aid in the early detection of MDD and GAD.

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.

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