A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study Article Swipe
Matthew Deady
,
Ray Duncan
,
Matthew Sonesen
,
Renier Estiandan
,
Kelly K. Stimpert
,
Sylvia Cho
,
Jeffrey Beers
,
Brian Goodness
,
Lance Daniel Jones
,
Richard A. Forshee
,
Steven A. Anderson
,
Hussein Ezzeldin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2196/54597
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2196/54597
Background Adverse events (AEs) associated with vaccination have traditionally been evaluated by epidemiological studies. More recently, they have gained attention due to the emergency use authorization of several COVID-19 vaccines. As part of its responsibility to conduct postmarket surveillance, the US Food and Drug Administration continues to monitor several AEs of interest to ensure the safety of vaccines, including those for COVID-19. Objective This study is part of the Biologics Effectiveness and Safety Initiative, which aims to improve the US Food and Drug Administration’s postmarket surveillance capabilities while minimizing the burden of collecting clinical data on suspected postvaccination AEs. The objective of this study was to enhance active surveillance efforts through a pilot platform that can receive automatically reported AE cases through a health care data exchange. Methods We detected cases by sharing and applying computable phenotype algorithms to real-world data in health care providers’ electronic health records databases. Using the fast healthcare interoperability resources standard for secure data transmission, we implemented a computable phenotype algorithm on a new health care system. The study focused on the algorithm's positive predictive value, validated through clinical records, assessing both the time required for implementation and the accuracy of AE detection. Results The algorithm required 200-250 hours to implement and optimize. Of the 6,574,420 clinical encounters across 694,151 patients, 30 cases were identified as potential myocarditis/pericarditis. Of these, 26 cases were retrievable, and 24 underwent clinical validation. In total, 14 cases were confirmed as definite or probable myocarditis/pericarditis, yielding a positive predictive value of 58.3% (95% CI 37.3%-76.9%). These findings underscore the algorithm's capability for real-time detection of AEs, though they also highlight variability in performance across different health care systems. Conclusions The study advocates for the ongoing refinement and application of distributed computable phenotype algorithms to enhance AE detection capabilities. These tools are crucial for comprehensive postmarket surveillance and improved vaccine safety monitoring. The outcomes suggest the need for further optimization to achieve more consistent results across diverse health care settings.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2196/54597
- OA Status
- gold
- Cited By
- 1
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402706754
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4402706754Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2196/54597Digital Object Identifier
- Title
-
A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation StudyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-21Full publication date if available
- Authors
-
Matthew Deady, Ray Duncan, Matthew Sonesen, Renier Estiandan, Kelly K. Stimpert, Sylvia Cho, Jeffrey Beers, Brian Goodness, Lance Daniel Jones, Richard A. Forshee, Steven A. Anderson, Hussein EzzeldinList of authors in order
- Landing page
-
https://doi.org/10.2196/54597Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.2196/54597Direct OA link when available
- Concepts
-
Myocarditis, Computer science, Pericarditis, Real world data, Algorithm, Medicine, Artificial intelligence, Data mining, Internal medicine, Data scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
17Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4402706754 |
|---|---|
| doi | https://doi.org/10.2196/54597 |
| ids.doi | https://doi.org/10.2196/54597 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/39586081 |
| ids.openalex | https://openalex.org/W4402706754 |
| fwci | 0.82872147 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D000328 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Adult |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D005260 |
| mesh[1].is_major_topic | False |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Female |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D006801 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Humans |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D008297 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Male |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D008875 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Middle Aged |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D000465 |
| mesh[5].is_major_topic | True |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Algorithms |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D057286 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Electronic Health Records |
| mesh[7].qualifier_ui | Q000139 |
| mesh[7].descriptor_ui | D009205 |
| mesh[7].is_major_topic | True |
| mesh[7].qualifier_name | chemically induced |
| mesh[7].descriptor_name | Myocarditis |
| mesh[8].qualifier_ui | Q000139 |
| mesh[8].descriptor_ui | D010493 |
| mesh[8].is_major_topic | True |
| mesh[8].qualifier_name | chemically induced |
| mesh[8].descriptor_name | Pericarditis |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D010641 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Phenotype |
| mesh[10].qualifier_ui | Q000379 |
| mesh[10].descriptor_ui | D011358 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | methods |
| mesh[10].descriptor_name | Product Surveillance, Postmarketing |
| mesh[11].qualifier_ui | Q000706 |
| mesh[11].descriptor_ui | D011358 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | statistics & numerical data |
| mesh[11].descriptor_name | Product Surveillance, Postmarketing |
| mesh[12].qualifier_ui | |
| mesh[12].descriptor_ui | D014481 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | |
| mesh[12].descriptor_name | United States |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D014486 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | United States Food and Drug Administration |
| mesh[14].qualifier_ui | Q000009 |
| mesh[14].descriptor_ui | D014611 |
| mesh[14].is_major_topic | True |
| mesh[14].qualifier_name | adverse effects |
| mesh[14].descriptor_name | Vaccination |
| mesh[15].qualifier_ui | |
| mesh[15].descriptor_ui | D000328 |
| mesh[15].is_major_topic | False |
| mesh[15].qualifier_name | |
| mesh[15].descriptor_name | Adult |
| mesh[16].qualifier_ui | |
| mesh[16].descriptor_ui | D005260 |
| mesh[16].is_major_topic | False |
| mesh[16].qualifier_name | |
| mesh[16].descriptor_name | Female |
| mesh[17].qualifier_ui | |
| mesh[17].descriptor_ui | D006801 |
| mesh[17].is_major_topic | False |
| mesh[17].qualifier_name | |
| mesh[17].descriptor_name | Humans |
| mesh[18].qualifier_ui | |
| mesh[18].descriptor_ui | D008297 |
| mesh[18].is_major_topic | False |
| mesh[18].qualifier_name | |
| mesh[18].descriptor_name | Male |
| mesh[19].qualifier_ui | |
| mesh[19].descriptor_ui | D008875 |
| mesh[19].is_major_topic | False |
| mesh[19].qualifier_name | |
| mesh[19].descriptor_name | Middle Aged |
| mesh[20].qualifier_ui | |
| mesh[20].descriptor_ui | D000465 |
| mesh[20].is_major_topic | True |
| mesh[20].qualifier_name | |
| mesh[20].descriptor_name | Algorithms |
| mesh[21].qualifier_ui | |
| mesh[21].descriptor_ui | D057286 |
| mesh[21].is_major_topic | False |
| mesh[21].qualifier_name | |
| mesh[21].descriptor_name | Electronic Health Records |
| mesh[22].qualifier_ui | Q000139 |
| mesh[22].descriptor_ui | D009205 |
| mesh[22].is_major_topic | True |
| mesh[22].qualifier_name | chemically induced |
| mesh[22].descriptor_name | Myocarditis |
| mesh[23].qualifier_ui | Q000139 |
| mesh[23].descriptor_ui | D010493 |
| mesh[23].is_major_topic | True |
| mesh[23].qualifier_name | chemically induced |
| mesh[23].descriptor_name | Pericarditis |
| mesh[24].qualifier_ui | |
| mesh[24].descriptor_ui | D010641 |
| mesh[24].is_major_topic | False |
| mesh[24].qualifier_name | |
| mesh[24].descriptor_name | Phenotype |
| mesh[25].qualifier_ui | Q000379 |
| mesh[25].descriptor_ui | D011358 |
| mesh[25].is_major_topic | False |
| mesh[25].qualifier_name | methods |
| mesh[25].descriptor_name | Product Surveillance, Postmarketing |
| mesh[26].qualifier_ui | Q000706 |
| mesh[26].descriptor_ui | D011358 |
| mesh[26].is_major_topic | False |
| mesh[26].qualifier_name | statistics & numerical data |
| mesh[26].descriptor_name | Product Surveillance, Postmarketing |
| mesh[27].qualifier_ui | |
| mesh[27].descriptor_ui | D014481 |
| mesh[27].is_major_topic | False |
| mesh[27].qualifier_name | |
| mesh[27].descriptor_name | United States |
| mesh[28].qualifier_ui | |
| mesh[28].descriptor_ui | D014486 |
| mesh[28].is_major_topic | False |
| mesh[28].qualifier_name | |
| mesh[28].descriptor_name | United States Food and Drug Administration |
| mesh[29].qualifier_ui | Q000009 |
| mesh[29].descriptor_ui | D014611 |
| mesh[29].is_major_topic | True |
| mesh[29].qualifier_name | adverse effects |
| mesh[29].descriptor_name | Vaccination |
| mesh[30].qualifier_ui | |
| mesh[30].descriptor_ui | D000328 |
| mesh[30].is_major_topic | False |
| mesh[30].qualifier_name | |
| mesh[30].descriptor_name | Adult |
| mesh[31].qualifier_ui | |
| mesh[31].descriptor_ui | D005260 |
| mesh[31].is_major_topic | False |
| mesh[31].qualifier_name | |
| mesh[31].descriptor_name | Female |
| mesh[32].qualifier_ui | |
| mesh[32].descriptor_ui | D006801 |
| mesh[32].is_major_topic | False |
| mesh[32].qualifier_name | |
| mesh[32].descriptor_name | Humans |
| mesh[33].qualifier_ui | |
| mesh[33].descriptor_ui | D008297 |
| mesh[33].is_major_topic | False |
| mesh[33].qualifier_name | |
| mesh[33].descriptor_name | Male |
| mesh[34].qualifier_ui | |
| mesh[34].descriptor_ui | D008875 |
| mesh[34].is_major_topic | False |
| mesh[34].qualifier_name | |
| mesh[34].descriptor_name | Middle Aged |
| mesh[35].qualifier_ui | |
| mesh[35].descriptor_ui | D000465 |
| mesh[35].is_major_topic | True |
| mesh[35].qualifier_name | |
| mesh[35].descriptor_name | Algorithms |
| mesh[36].qualifier_ui | |
| mesh[36].descriptor_ui | D057286 |
| mesh[36].is_major_topic | False |
| mesh[36].qualifier_name | |
| mesh[36].descriptor_name | Electronic Health Records |
| mesh[37].qualifier_ui | Q000139 |
| mesh[37].descriptor_ui | D009205 |
| mesh[37].is_major_topic | True |
| mesh[37].qualifier_name | chemically induced |
| mesh[37].descriptor_name | Myocarditis |
| mesh[38].qualifier_ui | Q000139 |
| mesh[38].descriptor_ui | D010493 |
| mesh[38].is_major_topic | True |
| mesh[38].qualifier_name | chemically induced |
| mesh[38].descriptor_name | Pericarditis |
| mesh[39].qualifier_ui | |
| mesh[39].descriptor_ui | D010641 |
| mesh[39].is_major_topic | False |
| mesh[39].qualifier_name | |
| mesh[39].descriptor_name | Phenotype |
| mesh[40].qualifier_ui | Q000379 |
| mesh[40].descriptor_ui | D011358 |
| mesh[40].is_major_topic | False |
| mesh[40].qualifier_name | methods |
| mesh[40].descriptor_name | Product Surveillance, Postmarketing |
| mesh[41].qualifier_ui | Q000706 |
| mesh[41].descriptor_ui | D011358 |
| mesh[41].is_major_topic | False |
| mesh[41].qualifier_name | statistics & numerical data |
| mesh[41].descriptor_name | Product Surveillance, Postmarketing |
| mesh[42].qualifier_ui | |
| mesh[42].descriptor_ui | D014481 |
| mesh[42].is_major_topic | False |
| mesh[42].qualifier_name | |
| mesh[42].descriptor_name | United States |
| mesh[43].qualifier_ui | |
| mesh[43].descriptor_ui | D014486 |
| mesh[43].is_major_topic | False |
| mesh[43].qualifier_name | |
| mesh[43].descriptor_name | United States Food and Drug Administration |
| mesh[44].qualifier_ui | Q000009 |
| mesh[44].descriptor_ui | D014611 |
| mesh[44].is_major_topic | True |
| mesh[44].qualifier_name | adverse effects |
| mesh[44].descriptor_name | Vaccination |
| mesh[45].qualifier_ui | |
| mesh[45].descriptor_ui | D000328 |
| mesh[45].is_major_topic | False |
| mesh[45].qualifier_name | |
| mesh[45].descriptor_name | Adult |
| mesh[46].qualifier_ui | |
| mesh[46].descriptor_ui | D005260 |
| mesh[46].is_major_topic | False |
| mesh[46].qualifier_name | |
| mesh[46].descriptor_name | Female |
| mesh[47].qualifier_ui | |
| mesh[47].descriptor_ui | D006801 |
| mesh[47].is_major_topic | False |
| mesh[47].qualifier_name | |
| mesh[47].descriptor_name | Humans |
| mesh[48].qualifier_ui | |
| mesh[48].descriptor_ui | D008297 |
| mesh[48].is_major_topic | False |
| mesh[48].qualifier_name | |
| mesh[48].descriptor_name | Male |
| mesh[49].qualifier_ui | |
| mesh[49].descriptor_ui | D008875 |
| mesh[49].is_major_topic | False |
| mesh[49].qualifier_name | |
| mesh[49].descriptor_name | Middle Aged |
| type | article |
| title | A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study |
| biblio.issue | |
| biblio.volume | 26 |
| biblio.last_page | e54597 |
| biblio.first_page | e54597 |
| topics[0].id | https://openalex.org/T11112 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9994999766349792 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2705 |
| topics[0].subfield.display_name | Cardiology and Cardiovascular Medicine |
| topics[0].display_name | Viral Infections and Immunology Research |
| topics[1].id | https://openalex.org/T12291 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9606000185012817 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2705 |
| topics[1].subfield.display_name | Cardiology and Cardiovascular Medicine |
| topics[1].display_name | Pericarditis and Cardiac Tamponade |
| topics[2].id | https://openalex.org/T10976 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9376000165939331 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2725 |
| topics[2].subfield.display_name | Infectious Diseases |
| topics[2].display_name | Viral gastroenteritis research and epidemiology |
| is_xpac | False |
| apc_list.value | 2950 |
| apc_list.currency | USD |
| apc_list.value_usd | 2950 |
| apc_paid.value | 2950 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 2950 |
| concepts[0].id | https://openalex.org/C2780875844 |
| concepts[0].level | 2 |
| concepts[0].score | 0.5574761629104614 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q186235 |
| concepts[0].display_name | Myocarditis |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5127512216567993 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C2781001862 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5069027543067932 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q501561 |
| concepts[2].display_name | Pericarditis |
| concepts[3].id | https://openalex.org/C3020493868 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5004267692565918 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q55631277 |
| concepts[3].display_name | Real world data |
| concepts[4].id | https://openalex.org/C11413529 |
| concepts[4].level | 1 |
| concepts[4].score | 0.48009946942329407 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[4].display_name | Algorithm |
| concepts[5].id | https://openalex.org/C71924100 |
| concepts[5].level | 0 |
| concepts[5].score | 0.417039155960083 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[5].display_name | Medicine |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3728440999984741 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C124101348 |
| concepts[7].level | 1 |
| concepts[7].score | 0.3337421417236328 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[7].display_name | Data mining |
| concepts[8].id | https://openalex.org/C126322002 |
| concepts[8].level | 1 |
| concepts[8].score | 0.2101874053478241 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[8].display_name | Internal medicine |
| concepts[9].id | https://openalex.org/C2522767166 |
| concepts[9].level | 1 |
| concepts[9].score | 0.14392849802970886 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2374463 |
| concepts[9].display_name | Data science |
| keywords[0].id | https://openalex.org/keywords/myocarditis |
| keywords[0].score | 0.5574761629104614 |
| keywords[0].display_name | Myocarditis |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5127512216567993 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/pericarditis |
| keywords[2].score | 0.5069027543067932 |
| keywords[2].display_name | Pericarditis |
| keywords[3].id | https://openalex.org/keywords/real-world-data |
| keywords[3].score | 0.5004267692565918 |
| keywords[3].display_name | Real world data |
| keywords[4].id | https://openalex.org/keywords/algorithm |
| keywords[4].score | 0.48009946942329407 |
| keywords[4].display_name | Algorithm |
| keywords[5].id | https://openalex.org/keywords/medicine |
| keywords[5].score | 0.417039155960083 |
| keywords[5].display_name | Medicine |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.3728440999984741 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/data-mining |
| keywords[7].score | 0.3337421417236328 |
| keywords[7].display_name | Data mining |
| keywords[8].id | https://openalex.org/keywords/internal-medicine |
| keywords[8].score | 0.2101874053478241 |
| keywords[8].display_name | Internal medicine |
| keywords[9].id | https://openalex.org/keywords/data-science |
| keywords[9].score | 0.14392849802970886 |
| keywords[9].display_name | Data science |
| language | en |
| locations[0].id | doi:10.2196/54597 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S17147534 |
| locations[0].source.issn | 1438-8871, 1439-4456 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1438-8871 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Journal of Medical Internet Research |
| locations[0].source.host_organization | https://openalex.org/P4310320608 |
| locations[0].source.host_organization_name | JMIR Publications |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320608 |
| locations[0].source.host_organization_lineage_names | JMIR Publications |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Journal of Medical Internet Research |
| locations[0].landing_page_url | https://doi.org/10.2196/54597 |
| locations[1].id | pmid:39586081 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Journal of medical Internet research |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/39586081 |
| locations[2].id | pmh:oai:doaj.org/article:85f2313e0df04449ab31be79a92bc5c8 |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Journal of Medical Internet Research, Vol 26, p e54597 (2024) |
| locations[2].landing_page_url | https://doaj.org/article/85f2313e0df04449ab31be79a92bc5c8 |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:11629037 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | J Med Internet Res |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11629037 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5025084486 |
| authorships[0].author.orcid | https://orcid.org/0009-0004-1761-8058 |
| authorships[0].author.display_name | Matthew Deady |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210126810 |
| authorships[0].affiliations[0].raw_affiliation_string | IBM Consulting, Bethesda, MD, United States. |
| authorships[0].institutions[0].id | https://openalex.org/I4210126810 |
| authorships[0].institutions[0].ror | https://ror.org/035fp5e48 |
| authorships[0].institutions[0].type | other |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210126810 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Scientific Consulting Group |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Matthew Deady |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | IBM Consulting, Bethesda, MD, United States. |
| authorships[1].author.id | https://openalex.org/A5054802330 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-6764-655X |
| authorships[1].author.display_name | Ray Duncan |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210149743 |
| authorships[1].affiliations[0].raw_affiliation_string | Departments of Enterprise Information Services and Pediatrics, Cedars-Sinai Health System, Los Angeles, CA, United States. |
| authorships[1].institutions[0].id | https://openalex.org/I4210149743 |
| authorships[1].institutions[0].ror | https://ror.org/05j8naw58 |
| authorships[1].institutions[0].type | healthcare |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210149743 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Sinai Health System |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Raymond Duncan |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Departments of Enterprise Information Services and Pediatrics, Cedars-Sinai Health System, Los Angeles, CA, United States. |
| authorships[2].author.id | https://openalex.org/A5028676473 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Matthew Sonesen |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210149743 |
| authorships[2].affiliations[0].raw_affiliation_string | Cedars-Sinai Health System, Enterprise Information Services, Los Angeles, CA, United States. |
| authorships[2].institutions[0].id | https://openalex.org/I4210149743 |
| authorships[2].institutions[0].ror | https://ror.org/05j8naw58 |
| authorships[2].institutions[0].type | healthcare |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210149743 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | Sinai Health System |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Matthew Sonesen |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Cedars-Sinai Health System, Enterprise Information Services, Los Angeles, CA, United States. |
| authorships[3].author.id | https://openalex.org/A5087709067 |
| authorships[3].author.orcid | https://orcid.org/0009-0000-7186-3207 |
| authorships[3].author.display_name | Renier Estiandan |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210149743 |
| authorships[3].affiliations[0].raw_affiliation_string | Cedars-Sinai Health System, Enterprise Information Services, Los Angeles, CA, United States. |
| authorships[3].institutions[0].id | https://openalex.org/I4210149743 |
| authorships[3].institutions[0].ror | https://ror.org/05j8naw58 |
| authorships[3].institutions[0].type | healthcare |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210149743 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | Sinai Health System |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Renier Estiandan |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Cedars-Sinai Health System, Enterprise Information Services, Los Angeles, CA, United States. |
| authorships[4].author.id | https://openalex.org/A5049622402 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-0440-1816 |
| authorships[4].author.display_name | Kelly K. Stimpert |
| authorships[4].affiliations[0].raw_affiliation_string | DRT Strategies, Inc., Arlington, VA, United States. |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Kelly Stimpert |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | DRT Strategies, Inc., Arlington, VA, United States. |
| authorships[5].author.id | https://openalex.org/A5030136150 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-0263-0343 |
| authorships[5].author.display_name | Sylvia Cho |
| authorships[5].countries | US |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I1318287680 |
| authorships[5].affiliations[0].raw_affiliation_string | Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD, United States. |
| authorships[5].institutions[0].id | https://openalex.org/I1318287680 |
| authorships[5].institutions[0].ror | https://ror.org/02nr3fr97 |
| authorships[5].institutions[0].type | facility |
| authorships[5].institutions[0].lineage | https://openalex.org/I1299022934, https://openalex.org/I1318287680, https://openalex.org/I1320320070 |
| authorships[5].institutions[0].country_code | US |
| authorships[5].institutions[0].display_name | Center for Biologics Evaluation and Research |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Sylvia Cho |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD, United States. |
| authorships[6].author.id | https://openalex.org/A5074439401 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-5363-0434 |
| authorships[6].author.display_name | Jeffrey Beers |
| authorships[6].countries | US |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I4210126810 |
| authorships[6].affiliations[0].raw_affiliation_string | IBM Consulting, Bethesda, MD, United States. |
| authorships[6].institutions[0].id | https://openalex.org/I4210126810 |
| authorships[6].institutions[0].ror | https://ror.org/035fp5e48 |
| authorships[6].institutions[0].type | other |
| authorships[6].institutions[0].lineage | https://openalex.org/I4210126810 |
| authorships[6].institutions[0].country_code | US |
| authorships[6].institutions[0].display_name | Scientific Consulting Group |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Jeffrey Beers |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | IBM Consulting, Bethesda, MD, United States. |
| authorships[7].author.id | https://openalex.org/A5026448564 |
| authorships[7].author.orcid | https://orcid.org/0009-0002-0626-9142 |
| authorships[7].author.display_name | Brian Goodness |
| authorships[7].countries | US |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I4210126810 |
| authorships[7].affiliations[0].raw_affiliation_string | IBM Consulting, Bethesda, MD, United States. |
| authorships[7].institutions[0].id | https://openalex.org/I4210126810 |
| authorships[7].institutions[0].ror | https://ror.org/035fp5e48 |
| authorships[7].institutions[0].type | other |
| authorships[7].institutions[0].lineage | https://openalex.org/I4210126810 |
| authorships[7].institutions[0].country_code | US |
| authorships[7].institutions[0].display_name | Scientific Consulting Group |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Brian Goodness |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | IBM Consulting, Bethesda, MD, United States. |
| authorships[8].author.id | https://openalex.org/A5055038966 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-4924-0123 |
| authorships[8].author.display_name | Lance Daniel Jones |
| authorships[8].countries | US |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I4210126810 |
| authorships[8].affiliations[0].raw_affiliation_string | IBM Consulting, Bethesda, MD, United States. |
| authorships[8].institutions[0].id | https://openalex.org/I4210126810 |
| authorships[8].institutions[0].ror | https://ror.org/035fp5e48 |
| authorships[8].institutions[0].type | other |
| authorships[8].institutions[0].lineage | https://openalex.org/I4210126810 |
| authorships[8].institutions[0].country_code | US |
| authorships[8].institutions[0].display_name | Scientific Consulting Group |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Lance Daniel Jones |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | IBM Consulting, Bethesda, MD, United States. |
| authorships[9].author.id | https://openalex.org/A5087332112 |
| authorships[9].author.orcid | https://orcid.org/0000-0002-5805-2837 |
| authorships[9].author.display_name | Richard A. Forshee |
| authorships[9].countries | US |
| authorships[9].affiliations[0].institution_ids | https://openalex.org/I1318287680 |
| authorships[9].affiliations[0].raw_affiliation_string | Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD, United States. |
| authorships[9].institutions[0].id | https://openalex.org/I1318287680 |
| authorships[9].institutions[0].ror | https://ror.org/02nr3fr97 |
| authorships[9].institutions[0].type | facility |
| authorships[9].institutions[0].lineage | https://openalex.org/I1299022934, https://openalex.org/I1318287680, https://openalex.org/I1320320070 |
| authorships[9].institutions[0].country_code | US |
| authorships[9].institutions[0].display_name | Center for Biologics Evaluation and Research |
| authorships[9].author_position | middle |
| authorships[9].raw_author_name | Richard Forshee |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD, United States. |
| authorships[10].author.id | https://openalex.org/A5008758398 |
| authorships[10].author.orcid | |
| authorships[10].author.display_name | Steven A. Anderson |
| authorships[10].countries | US |
| authorships[10].affiliations[0].institution_ids | https://openalex.org/I1318287680 |
| authorships[10].affiliations[0].raw_affiliation_string | Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD, United States. |
| authorships[10].institutions[0].id | https://openalex.org/I1318287680 |
| authorships[10].institutions[0].ror | https://ror.org/02nr3fr97 |
| authorships[10].institutions[0].type | facility |
| authorships[10].institutions[0].lineage | https://openalex.org/I1299022934, https://openalex.org/I1318287680, https://openalex.org/I1320320070 |
| authorships[10].institutions[0].country_code | US |
| authorships[10].institutions[0].display_name | Center for Biologics Evaluation and Research |
| authorships[10].author_position | middle |
| authorships[10].raw_author_name | Steven A Anderson |
| authorships[10].is_corresponding | False |
| authorships[10].raw_affiliation_strings | Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD, United States. |
| authorships[11].author.id | https://openalex.org/A5001244005 |
| authorships[11].author.orcid | https://orcid.org/0000-0001-7375-6456 |
| authorships[11].author.display_name | Hussein Ezzeldin |
| authorships[11].countries | US |
| authorships[11].affiliations[0].institution_ids | https://openalex.org/I1318287680 |
| authorships[11].affiliations[0].raw_affiliation_string | Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD, United States. |
| authorships[11].institutions[0].id | https://openalex.org/I1318287680 |
| authorships[11].institutions[0].ror | https://ror.org/02nr3fr97 |
| authorships[11].institutions[0].type | facility |
| authorships[11].institutions[0].lineage | https://openalex.org/I1299022934, https://openalex.org/I1318287680, https://openalex.org/I1320320070 |
| authorships[11].institutions[0].country_code | US |
| authorships[11].institutions[0].display_name | Center for Biologics Evaluation and Research |
| authorships[11].author_position | last |
| authorships[11].raw_author_name | Hussein Ezzeldin |
| authorships[11].is_corresponding | False |
| authorships[11].raw_affiliation_strings | Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD, United States. |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.2196/54597 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-25T14:43:58.451035 |
| primary_topic.id | https://openalex.org/T11112 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9994999766349792 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2705 |
| primary_topic.subfield.display_name | Cardiology and Cardiovascular Medicine |
| primary_topic.display_name | Viral Infections and Immunology Research |
| related_works | https://openalex.org/W2051487156, https://openalex.org/W2073681303, https://openalex.org/W4365144739, https://openalex.org/W1983595028, https://openalex.org/W2360905633, https://openalex.org/W2789461249, https://openalex.org/W2154960548, https://openalex.org/W2153527439, https://openalex.org/W2033095565, https://openalex.org/W2154820798 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 4 |
| best_oa_location.id | doi:10.2196/54597 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S17147534 |
| best_oa_location.source.issn | 1438-8871, 1439-4456 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1438-8871 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Journal of Medical Internet Research |
| best_oa_location.source.host_organization | https://openalex.org/P4310320608 |
| best_oa_location.source.host_organization_name | JMIR Publications |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320608 |
| best_oa_location.source.host_organization_lineage_names | JMIR Publications |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Journal of Medical Internet Research |
| best_oa_location.landing_page_url | https://doi.org/10.2196/54597 |
| primary_location.id | doi:10.2196/54597 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S17147534 |
| primary_location.source.issn | 1438-8871, 1439-4456 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1438-8871 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Journal of Medical Internet Research |
| primary_location.source.host_organization | https://openalex.org/P4310320608 |
| primary_location.source.host_organization_name | JMIR Publications |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320608 |
| primary_location.source.host_organization_lineage_names | JMIR Publications |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Journal of Medical Internet Research |
| primary_location.landing_page_url | https://doi.org/10.2196/54597 |
| publication_date | 2024-09-21 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W3172260675, https://openalex.org/W2146579584, https://openalex.org/W4399025576, https://openalex.org/W1966976587, https://openalex.org/W3080627676, https://openalex.org/W4207051048, https://openalex.org/W4283367577, https://openalex.org/W2067978628, https://openalex.org/W2162445884, https://openalex.org/W4251112759, https://openalex.org/W4294214983, https://openalex.org/W4210330852, https://openalex.org/W4288697567, https://openalex.org/W3090706124, https://openalex.org/W4225272053, https://openalex.org/W4376641438, https://openalex.org/W3195573207 |
| referenced_works_count | 17 |
| abstract_inverted_index.a | 111, 122, 162, 167, 246 |
| abstract_inverted_index.14 | 236 |
| abstract_inverted_index.24 | 230 |
| abstract_inverted_index.26 | 225 |
| abstract_inverted_index.30 | 216 |
| abstract_inverted_index.AE | 119, 196, 295 |
| abstract_inverted_index.As | 30 |
| abstract_inverted_index.CI | 253 |
| abstract_inverted_index.In | 234 |
| abstract_inverted_index.Of | 208, 223 |
| abstract_inverted_index.US | 40, 79 |
| abstract_inverted_index.We | 128 |
| abstract_inverted_index.as | 220, 240 |
| abstract_inverted_index.by | 11, 131 |
| abstract_inverted_index.in | 141, 271 |
| abstract_inverted_index.is | 65 |
| abstract_inverted_index.of | 26, 32, 50, 56, 67, 91, 101, 195, 250, 264, 288 |
| abstract_inverted_index.on | 95, 166, 175 |
| abstract_inverted_index.or | 242 |
| abstract_inverted_index.to | 21, 35, 46, 52, 76, 105, 138, 204, 293, 319 |
| abstract_inverted_index.we | 160 |
| abstract_inverted_index.AEs | 49 |
| abstract_inverted_index.The | 99, 172, 199, 279, 311 |
| abstract_inverted_index.and | 42, 71, 81, 133, 192, 206, 229, 286, 306 |
| abstract_inverted_index.are | 300 |
| abstract_inverted_index.can | 115 |
| abstract_inverted_index.due | 20 |
| abstract_inverted_index.for | 60, 156, 190, 261, 282, 302, 316 |
| abstract_inverted_index.its | 33 |
| abstract_inverted_index.new | 168 |
| abstract_inverted_index.the | 22, 39, 54, 68, 78, 89, 150, 176, 187, 193, 209, 258, 283, 314 |
| abstract_inverted_index.use | 24 |
| abstract_inverted_index.was | 104 |
| abstract_inverted_index.(95% | 252 |
| abstract_inverted_index.AEs, | 265 |
| abstract_inverted_index.AEs. | 98 |
| abstract_inverted_index.Drug | 43, 82 |
| abstract_inverted_index.Food | 41, 80 |
| abstract_inverted_index.More | 14 |
| abstract_inverted_index.This | 63 |
| abstract_inverted_index.aims | 75 |
| abstract_inverted_index.also | 268 |
| abstract_inverted_index.been | 9 |
| abstract_inverted_index.both | 186 |
| abstract_inverted_index.care | 124, 143, 170, 276, 327 |
| abstract_inverted_index.data | 94, 125, 140, 158 |
| abstract_inverted_index.fast | 151 |
| abstract_inverted_index.have | 7, 17 |
| abstract_inverted_index.more | 321 |
| abstract_inverted_index.need | 315 |
| abstract_inverted_index.part | 31, 66 |
| abstract_inverted_index.that | 114 |
| abstract_inverted_index.they | 16, 267 |
| abstract_inverted_index.this | 102 |
| abstract_inverted_index.time | 188 |
| abstract_inverted_index.were | 218, 227, 238 |
| abstract_inverted_index.with | 5 |
| abstract_inverted_index.(AEs) | 3 |
| abstract_inverted_index.58.3% | 251 |
| abstract_inverted_index.These | 255, 298 |
| abstract_inverted_index.Using | 149 |
| abstract_inverted_index.cases | 120, 130, 217, 226, 237 |
| abstract_inverted_index.hours | 203 |
| abstract_inverted_index.pilot | 112 |
| abstract_inverted_index.study | 64, 103, 173, 280 |
| abstract_inverted_index.those | 59 |
| abstract_inverted_index.tools | 299 |
| abstract_inverted_index.value | 249 |
| abstract_inverted_index.which | 74 |
| abstract_inverted_index.while | 87 |
| abstract_inverted_index.Safety | 72 |
| abstract_inverted_index.across | 213, 273, 324 |
| abstract_inverted_index.active | 107 |
| abstract_inverted_index.burden | 90 |
| abstract_inverted_index.ensure | 53 |
| abstract_inverted_index.events | 2 |
| abstract_inverted_index.gained | 18 |
| abstract_inverted_index.health | 123, 142, 146, 169, 275, 326 |
| abstract_inverted_index.safety | 55, 309 |
| abstract_inverted_index.secure | 157 |
| abstract_inverted_index.these, | 224 |
| abstract_inverted_index.though | 266 |
| abstract_inverted_index.total, | 235 |
| abstract_inverted_index.value, | 180 |
| abstract_inverted_index.200-250 | 202 |
| abstract_inverted_index.694,151 | 214 |
| abstract_inverted_index.Adverse | 1 |
| abstract_inverted_index.Methods | 127 |
| abstract_inverted_index.Results | 198 |
| abstract_inverted_index.achieve | 320 |
| abstract_inverted_index.conduct | 36 |
| abstract_inverted_index.crucial | 301 |
| abstract_inverted_index.diverse | 325 |
| abstract_inverted_index.efforts | 109 |
| abstract_inverted_index.enhance | 106, 294 |
| abstract_inverted_index.focused | 174 |
| abstract_inverted_index.further | 317 |
| abstract_inverted_index.improve | 77 |
| abstract_inverted_index.monitor | 47 |
| abstract_inverted_index.ongoing | 284 |
| abstract_inverted_index.receive | 116 |
| abstract_inverted_index.records | 147 |
| abstract_inverted_index.results | 323 |
| abstract_inverted_index.several | 27, 48 |
| abstract_inverted_index.sharing | 132 |
| abstract_inverted_index.suggest | 313 |
| abstract_inverted_index.system. | 171 |
| abstract_inverted_index.through | 110, 121, 182 |
| abstract_inverted_index.vaccine | 308 |
| abstract_inverted_index.COVID-19 | 28 |
| abstract_inverted_index.accuracy | 194 |
| abstract_inverted_index.applying | 134 |
| abstract_inverted_index.clinical | 93, 183, 211, 232 |
| abstract_inverted_index.definite | 241 |
| abstract_inverted_index.detected | 129 |
| abstract_inverted_index.findings | 256 |
| abstract_inverted_index.improved | 307 |
| abstract_inverted_index.interest | 51 |
| abstract_inverted_index.outcomes | 312 |
| abstract_inverted_index.platform | 113 |
| abstract_inverted_index.positive | 178, 247 |
| abstract_inverted_index.probable | 243 |
| abstract_inverted_index.records, | 184 |
| abstract_inverted_index.reported | 118 |
| abstract_inverted_index.required | 189, 201 |
| abstract_inverted_index.standard | 155 |
| abstract_inverted_index.studies. | 13 |
| abstract_inverted_index.systems. | 277 |
| abstract_inverted_index.yielding | 245 |
| abstract_inverted_index.6,574,420 | 210 |
| abstract_inverted_index.Biologics | 69 |
| abstract_inverted_index.COVID-19. | 61 |
| abstract_inverted_index.Objective | 62 |
| abstract_inverted_index.advocates | 281 |
| abstract_inverted_index.algorithm | 165, 200 |
| abstract_inverted_index.assessing | 185 |
| abstract_inverted_index.attention | 19 |
| abstract_inverted_index.confirmed | 239 |
| abstract_inverted_index.continues | 45 |
| abstract_inverted_index.detection | 263, 296 |
| abstract_inverted_index.different | 274 |
| abstract_inverted_index.emergency | 23 |
| abstract_inverted_index.evaluated | 10 |
| abstract_inverted_index.exchange. | 126 |
| abstract_inverted_index.highlight | 269 |
| abstract_inverted_index.implement | 205 |
| abstract_inverted_index.including | 58 |
| abstract_inverted_index.objective | 100 |
| abstract_inverted_index.optimize. | 207 |
| abstract_inverted_index.patients, | 215 |
| abstract_inverted_index.phenotype | 136, 164, 291 |
| abstract_inverted_index.potential | 221 |
| abstract_inverted_index.real-time | 262 |
| abstract_inverted_index.recently, | 15 |
| abstract_inverted_index.resources | 154 |
| abstract_inverted_index.settings. | 328 |
| abstract_inverted_index.suspected | 96 |
| abstract_inverted_index.underwent | 231 |
| abstract_inverted_index.vaccines, | 57 |
| abstract_inverted_index.vaccines. | 29 |
| abstract_inverted_index.validated | 181 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.algorithms | 137, 292 |
| abstract_inverted_index.associated | 4 |
| abstract_inverted_index.capability | 260 |
| abstract_inverted_index.collecting | 92 |
| abstract_inverted_index.computable | 135, 163, 290 |
| abstract_inverted_index.consistent | 322 |
| abstract_inverted_index.databases. | 148 |
| abstract_inverted_index.detection. | 197 |
| abstract_inverted_index.electronic | 145 |
| abstract_inverted_index.encounters | 212 |
| abstract_inverted_index.healthcare | 152 |
| abstract_inverted_index.identified | 219 |
| abstract_inverted_index.minimizing | 88 |
| abstract_inverted_index.postmarket | 37, 84, 304 |
| abstract_inverted_index.predictive | 179, 248 |
| abstract_inverted_index.real-world | 139 |
| abstract_inverted_index.refinement | 285 |
| abstract_inverted_index.underscore | 257 |
| abstract_inverted_index.Conclusions | 278 |
| abstract_inverted_index.Initiative, | 73 |
| abstract_inverted_index.algorithm's | 177, 259 |
| abstract_inverted_index.application | 287 |
| abstract_inverted_index.distributed | 289 |
| abstract_inverted_index.implemented | 161 |
| abstract_inverted_index.monitoring. | 310 |
| abstract_inverted_index.performance | 272 |
| abstract_inverted_index.vaccination | 6 |
| abstract_inverted_index.validation. | 233 |
| abstract_inverted_index.variability | 270 |
| abstract_inverted_index.capabilities | 86 |
| abstract_inverted_index.optimization | 318 |
| abstract_inverted_index.providers’ | 144 |
| abstract_inverted_index.retrievable, | 228 |
| abstract_inverted_index.surveillance | 85, 108, 305 |
| abstract_inverted_index.37.3%-76.9%). | 254 |
| abstract_inverted_index.Effectiveness | 70 |
| abstract_inverted_index.authorization | 25 |
| abstract_inverted_index.automatically | 117 |
| abstract_inverted_index.capabilities. | 297 |
| abstract_inverted_index.comprehensive | 303 |
| abstract_inverted_index.surveillance, | 38 |
| abstract_inverted_index.traditionally | 8 |
| abstract_inverted_index.transmission, | 159 |
| abstract_inverted_index.Administration | 44 |
| abstract_inverted_index.implementation | 191 |
| abstract_inverted_index.responsibility | 34 |
| abstract_inverted_index.epidemiological | 12 |
| abstract_inverted_index.postvaccination | 97 |
| abstract_inverted_index.interoperability | 153 |
| abstract_inverted_index.Administration’s | 83 |
| abstract_inverted_index.myocarditis/pericarditis, | 244 |
| abstract_inverted_index.myocarditis/pericarditis. | 222 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 12 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.7124799 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |