2021
DOI: 10.2196/25457
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Classification of the Disposition of Patients Hospitalized with COVID-19: Reading Discharge Summaries Using Natural Language Processing

Abstract: Background Medical notes are a rich source of patient data; however, the nature of unstructured text has largely precluded the use of these data for large retrospective analyses. Transforming clinical text into structured data can enable large-scale research studies with electronic health records (EHR) data. Natural language processing (NLP) can be used for text information retrieval, reducing the need for labor-intensive chart review. Here we present an application of NLP to large-scale analysis o… Show more

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Cited by 10 publications
(7 citation statements)
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“…For instance, using a combination of NLP and ML methods enables the prediction of potential ICU admissions from the EHRs of patients with COVID-19 [ 37 ]. Another study used hospital discharge summary notes to develop an NLP pipeline to categorize the discharge dispositions of such patients [ 38 ]. Within the Department of Veterans Affairs (VA), a study developed an NLP system to extract possible positive COVID-19 cases from clinical text [ 39 ].…”
Section: Nlp For Electronic Health Records (Ehrs)mentioning
confidence: 99%
“…For instance, using a combination of NLP and ML methods enables the prediction of potential ICU admissions from the EHRs of patients with COVID-19 [ 37 ]. Another study used hospital discharge summary notes to develop an NLP pipeline to categorize the discharge dispositions of such patients [ 38 ]. Within the Department of Veterans Affairs (VA), a study developed an NLP system to extract possible positive COVID-19 cases from clinical text [ 39 ].…”
Section: Nlp For Electronic Health Records (Ehrs)mentioning
confidence: 99%
“…Using tools built around ontologies (controlled vocabularies), like SNOMED-CT (Systematized Nomenclature of Medicine-Clinical Terms) [11], NLP has enabled researchers to automate the capture of information in clinical narratives [10]. This data mining of EHRs can be useful for detecting patterns in patient care, such as choice of treatments, adherence, and changes in functioning and well-being over time [12], which can predict patient treatment habits and their symptom outcomes [13], as well as patient outcomes [14].…”
Section: Nlp As a Tool For Understanding Depressionmentioning
confidence: 99%
“…ARDSFlag uses machine learning (ML) and natural language processing (NLP) techniques to evaluate Berlin criteria. ML and NLP have been proven to offer strong potential for identifying and predicting complex medical conditions by incorporating EHR data [13][14][15][16] . We also develop a visualization that integrates all components of the Berlin criteria in one graph.…”
Section: Introductionmentioning
confidence: 99%