Using word embeddings to expand terminology of dietary supplements on clinical notes

Fan, Yadan; Pakhomov, Serguei; McEwan, Reed; Zhao, Wendi; Lindemann, Elizabeth; Zhang, Rui

doi:10.1093/jamiaopen/ooz007

Cited by 48 publications

(15 citation statements)

References 21 publications

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“…This was achieved by using a predefined clinical lexicon for query expansion using a word2vec model trained by Pakhomov et al (2016). Similarly, Fan et al (2019) trained a word2vec model on clinical notes to expand dietary supplement vocabulary by finding corresponding misspellings and brand names, which helped retrieve more relevant documents.…”

Section: Word Embeddings and Query Expansionmentioning

confidence: 99%

NLP Methods for Extraction of Symptoms from Unstructured Data for Use in Prognostic COVID-19 Analytic Models

Silverman¹,

Sahoo

Ingraham

et al. 2021

jair

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Statistical modeling of outcomes based on a patient's presenting symptoms (symptomatology) can help deliver high quality care and allocate essential resources, which is especially important during the COVID-19 pandemic. Patient symptoms are typically found in unstructured notes, and thus not readily available for clinical decision making. In an attempt to fill this gap, this study compared two methods for symptom extraction from Emergency Department (ED) admission notes. Both methods utilized a lexicon derived by expanding The Center for Disease Control and Prevention's (CDC) Symptoms of Coronavirus list. The first method utilized a word2vec model to expand the lexicon using a dictionary mapping to the Uni ed Medical Language System (UMLS). The second method utilized the expanded lexicon as a rule-based gazetteer and the UMLS. These methods were evaluated against a manually annotated reference (f1-score of 0.87 for UMLS-based ensemble; and 0.85 for rule-based gazetteer with UMLS). Through analyses of associations of extracted symptoms used as features against various outcomes, salient risks among the population of COVID-19 patients, including increased risk of in-hospital mortality (OR 1.85, p-value < 0.001), were identified for patients presenting with dyspnea. Disparities between English and non-English speaking patients were also identified, the most salient being a concerning finding of opposing risk signals between fatigue and in-hospital mortality (non-English: OR 1.95, p-value = 0.02; English: OR 0.63, p-value = 0.01). While use of symptomatology for modeling of outcomes is not unique, unlike previous studies this study showed that models built using symptoms with the outcome of in-hospital mortality were not significantly different from models using data collected during an in-patient encounter (AUC of 0.9 with 95% CI of [0.88, 0.91] using only vital signs; AUC of 0.87 with 95% CI of [0.85, 0.88] using only symptoms). These findings indicate that prognostic models based on symptomatology could aid in extending COVID-19 patient care through telemedicine, replacing the need for in-person options. The methods presented in this study have potential for use in development of symptomatology-based models for other diseases, including for the study of Post-Acute Sequelae of COVID-19 (PASC).

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Section: Word Embeddings and Query Expansionmentioning

confidence: 99%

NLP Methods for Extraction of Symptoms from Unstructured Data for Use in Prognostic COVID-19 Analytic Models

Silverman¹,

Sahoo

Ingraham

et al. 2021

jair

Self Cite

View full text Add to dashboard Cite

show abstract

“…These will often outperform general-domain embeddings on clinical text mining tasks (51). Specialized clinical text embeddings have been used to improve clinical named entity recognition (75), resolve abbreviations in clinical text (76), expand a structured lexicon of radiology terms (77) and build a lexicon of dietary supplements (78). Second, an embedding can incorporate structured information beyond what is found in the text (79), and embeddings have been created to represent CUIs (80), documents (81,82), or entire patient records (83).…”

Section: Word Phrase and Character Embeddingsmentioning

confidence: 99%

Modern Clinical Text Mining: A Guide and Review

Percha

2021

Annu. Rev. Biomed. Data Sci.

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Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g., physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, this review describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation in health systems and in industry. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…Word embeddings are good at enriching the terminology of existing concepts. They are used in [24] to extend the terminology of dietary supplements. The experimental results prove that the expanded terms are more relevant as search keywords in clinical notes than in external knowledge sources.…”

Section: Related Workmentioning

confidence: 99%

Early Detection of Severe Flu Outbreaks using Contextual Word Embeddings

Karsi¹,

Zaim²,

Alami³

2021

IJACSA

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The purpose of automated health surveillance systems is to predict the emergence of a disease. In most cases, these systems use a text categorization model to classify any clinical text into a category corresponding to an illness. The problem arises when the target classes refer to diseases sharing multiple information such as symptoms. Thus, the classifier will have difficulty discriminating the disease under surveillance from other conditions of the same family, causing an increase in misclassification rate. Clinical texts contain keywords carrying relevant information to distinguish diseases with similar symptoms. However, these specific words are rare and sparse. Therefore, they have a minor impact on machine learning models' performance. Assuming that emphasizing specific terms contributes to improving classification performance, we propose an algorithm that enriches training samples with terms semantically similar to specific terms using the deep contextualized word embeddings ELMo. Next, we devise a weighting scheme combining chi-square and semantic scores to reflect the relatedness between features and the disease under surveillance. We evaluate our model using the SVM algorithm trained on i2b2 dataset supplemented by documents collected from Ibn Sina hospital in Rabat. Experimental results show a clear improvement in classification performance than baseline methods with an F-measure reaching 86.54%.

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Using word embeddings to expand terminology of dietary supplements on clinical notes

Cited by 48 publications

References 21 publications

NLP Methods for Extraction of Symptoms from Unstructured Data for Use in Prognostic COVID-19 Analytic Models

NLP Methods for Extraction of Symptoms from Unstructured Data for Use in Prognostic COVID-19 Analytic Models

Modern Clinical Text Mining: A Guide and Review

Early Detection of Severe Flu Outbreaks using Contextual Word Embeddings

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