COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model

Wang, Jingqi; Abu-El-Rub, Noor; Gray, J. W.; Pham, Huy Anh; Zhou, Yujia; Manion, Frank J.; Лю, Мэй; Song, Xing; Xu, Hua; Rouhizadeh, Masoud; Zhang, Yaoyun

doi:10.1093/jamia/ocab015

Cited by 52 publications

(35 citation statements)

References 13 publications

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“… 130 Of the remaining studies, 1 used ANN to perform a drive-through mass vaccination simulation, 138 while the other 4 used NLP methods and tools on various research topics, including cross-lingual clinical deidentification in electronic health records (EHRs), 139 dream reports analysis, 140 drug safety analysis by mining the FDA adverse event system, 141 COVID-19 clinical concept (signs and symptoms) identification, and normalization in EHRs. 142 …”

Section: Resultsmentioning

confidence: 99%

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review

Guo

Zhang

Lyu

et al. 2021

Journal of the American Medical Informatics Association

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Objective To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling. Materials and Methods We searched 2 major COVID-19 literature databases, the National Institutes of Health’s LitCovid and the World Health Organization’s COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening. Results In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications. Discussion Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias. Conclusion There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.

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Section: Resultsmentioning

confidence: 99%

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review

Guo

Zhang

Lyu

et al. 2021

Journal of the American Medical Informatics Association

Self Cite

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“…34 Our study also used an NLP-based approach which is well-suited to systematically process unstructured data containing post-acute COVID-19 symptoms. 8 Compared to machine learning-based approaches for symptom extraction, 23 which may focus on specific symptoms as unique classification tasks, requiring resource-intensive document annotation, a knowledgebased NLP approach can leverage existing knowledge bases to investigate and identify a wide variety of symptoms from a large dataset. This has also made our study distinct from previous work studying post-COVID-19 sequalae in specific patient populations (e.g., with neuropsychiatric outcomes) 14 or following a specific level of COVID-19 infection acuity (e.g., necessitating ICU admission).…”

Section: Discussionmentioning

confidence: 99%

Section: Background and Significancementioning

confidence: 99%

“…20,21 Several NLP approaches have been developed to extract COVID-19 signs or symptoms using either lexicon-based or machine learning-based approaches. 22,23 However, these approaches have focused on acute phases of COVID-19, while post-acute and long-term symptom identification were not covered and remain an unmet need. A significant challenge for developing such an NLP tool is the wide variation in potential post-acute COVID-19 symptoms, as COVID-19 survivors can experience a heterogeneous constellation of respiratory, cardiovascular, neurologic, psychiatric, dermatologic, and gastrointestinal symptoms and/or complications.…”

Section: Background and Significancementioning

confidence: 99%

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Development of a Post-Acute Sequelae of COVID-19 (PASC) Symptom Lexicon Using Electronic Health Record Clinical Notes

Wang

Foer

MacPhaul

et al. 2021

Preprint

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Objective: To develop a comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon from clinical notes to support PASC symptom identification and research. Methods: We identified 26,117 COVID-19 positive patients from the Mass General Brigham's electronic health records (EHR) and extracted 328,879 clinical notes from their post-acute infection period (day 51-110 from first positive COVID-19 test). The PASC symptom lexicon incorporated Unified Medical Language System (UMLS) Metathesaurus concepts and synonyms based on selected semantic types. The MTERMS natural language processing (NLP) tool was used to automatically extract symptoms from a development dataset. The lexicon was iteratively revised with manual chart review, keyword search, concept consolidation, and evaluation of NLP output. We assessed the comprehensiveness of the lexicon and the NLP performance using a validation dataset and reported the symptom prevalence across the entire corpus. Results: The PASC symptom lexicon included 355 symptoms consolidated from 1,520 UMLS concepts. NLP achieved an averaged precision of 0.94 and an estimated recall of 0.84. Symptoms with the highest frequency included pain (43.1%), anxiety (25.8%), depression (24.0%), fatigue (23.4%), joint pain (21.0%), shortness of breath (20.8%), headache (20.0%), nausea and/or vomiting (19.9%), myalgia (19.0%), and gastroesophageal reflux (18.6%). Discussion and Conclusion: PASC symptoms are diverse. A comprehensive PASC symptom lexicon can be derived using a data-driven, ontology-driven and NLP-assisted approach. By using unstructured data, this approach may improve identification and analysis of patient symptoms in the EHR, and inform prospective study design, preventative care strategies, and therapeutic interventions for patient care.

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“…Some health care data begin as natural language. The OHDSI community has applied natural language processing to translate text into OMOP tables and fields [7][8][9][10], but much work remains.…”

Section: Observational Research Infrastructurementioning

confidence: 99%

Drawing Reproducible Conclusions from Observational Clinical Data with OHDSI

et al. 2021

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Summary Objective: The current observational research literature shows extensive publication bias and contradiction. The Observational Health Data Sciences and Informatics (OHDSI) initiative seeks to improve research reproducibility through open science. Methods: OHDSI has created an international federated data source of electronic health records and administrative claims that covers nearly 10% of the world’s population. Using a common data model with a practical schema and extensive vocabulary mappings, data from around the world follow the identical format. OHDSI’s research methods emphasize reproducibility, with a large-scale approach to addressing confounding using propensity score adjustment with extensive diagnostics; negative and positive control hypotheses to test for residual systematic error; a variety of data sources to assess consistency and generalizability; a completely open approach including protocol, software, models, parameters, and raw results so that studies can be externally verified; and the study of many hypotheses in parallel so that the operating characteristics of the methods can be assessed. Results: OHDSI has already produced findings in areas like hypertension treatment that are being incorporated into practice, and it has produced rigorous studies of COVID-19 that have aided government agencies in their treatment decisions, that have characterized the disease extensively, that have estimated the comparative effects of treatments, and that the predict likelihood of advancing to serious complications. Conclusions: OHDSI practices open science and incorporates a series of methods to address reproducibility. It has produced important results in several areas, including hypertension therapy and COVID-19 research.

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COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model

Cited by 52 publications

References 13 publications

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review

Development of a Post-Acute Sequelae of COVID-19 (PASC) Symptom Lexicon Using Electronic Health Record Clinical Notes

Drawing Reproducible Conclusions from Observational Clinical Data with OHDSI

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