ArticleInternational journal of medical informatics2019
The use of natural language processing to identify Tdap-related local reactions at five health care systems in the Vaccine Safety Datalink.
Article in International journal of medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 27 citations in OpenAlex.
- Identification and validation of respiratory virus immunization using natural language processing.Frontiers in digital health · 2026Article
- Natural Language Processing Versus Diagnosis Code-Based Methods for Postherpetic Neuralgia Identification: Algorithm Development and Validation.JMIR medical informatics · 2024Article
- Article
- Identification of recurrent atrial fibrillation using natural language processing applied to electronic health records.European heart journal. Quality of care & clinical outcomes · 2024Article
- Research data warehouse: using electronic health records to conduct population-based observational studies.JAMIA open · 2023Article
- Identification of Preterm Labor Evaluation Visits and Extraction of Cervical Length Measures from Electronic Health Records Within a Large Integrated Health Care System: Algorithm Development and Validation.JMIR medical informatics · 2022Article
- Identifying Cases of Shoulder Injury Related to Vaccine Administration (SIRVA) in the United States: Development and Validation of a Natural Language Processing Method.JMIR public health and surveillance · 2022Article
- Risk for Shoulder Conditions After Vaccination: A Population-Based Study Using Real-World Data.Annals of internal medicine · 2022Article
- The Food and Drug Administration Biologics Effectiveness and Safety Initiative Facilitates Detection of Vaccine Administrations From Unstructured Data in Medical Records Through Natural Language Processing.Frontiers in digital health · 2021Article
- Automated Identification and Extraction of Exercise Treadmill Test Results.Journal of the American Heart Association · 2020Article
- The use of natural language processing to identify vaccine-related anaphylaxis at five health care systems in the Vaccine Safety Datalink.Pharmacoepidemiology and drug safety · 2020Article
- Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development.Frontiers in artificial intelligence · 2020Review
Corrections and comments
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Authors and funding
16 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveLocal reactions are the most common vaccine-related adverse event. There is no specific diagnosis code for local reaction due to vaccination. Previous vaccine safety studies used non-specific diagnosis codes to identify potential local reaction cases and confirmed the cases through manual chart review. In this study, a natural language processing (NLP) algorithm was developed to identify local reaction associated with tetanus-diphtheria-acellular pertussis (Tdap) vaccine in the Vaccine Safety Datalink.
methodsPresumptive cases of local reactions were identified among members ≥ 11 years of age using ICD-9-CM codes in all care settings in the 1-6 days following a Tdap vaccination between 2012 and 2014. The clinical notes were searched for signs and symptoms consistent with local reaction. Information on the timing and the location of a sign or symptom was also extracted to help determine whether or not the sign or symptom was vaccine related. Reactions triggered by causes other than Tdap vaccination were excluded. The NLP algorithm was developed at the lead study site and validated on a stratified random sample of 500 patients from five institutions.
resultsThe NLP algorithm achieved an overall weighted sensitivity of 87.9%, specificity of 92.8%, positive predictive value of 82.7%, and negative predictive value of 95.1%. In addition, using data at one site, the NLP algorithm identified 3326 potential Tdap-related local reactions that were not identified through diagnosis codes.
conclusionThe NLP algorithm achieved high accuracy, and demonstrated the potential of NLP to reduce the efforts of manual chart review in vaccine safety studies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.