Longitudinal Analysis of Electronic Health Information to Identify Possible COVID-19 Sequelae
Ongoing symptoms might follow acute COVID-19. Using electronic health information, we compared pre‒ and post‒COVID-19 diagnostic codes to identify symptoms that had higher encounter incidence in the post‒COVID-19 period as sequelae. This method can be used for hypothesis generation and ongoing monitoring of sequelae of COVID-19 and future emerging diseases..
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:29 |
|---|---|
| Enthalten in: |
Emerging Infectious Diseases - 29(2023), 2, Seite 389-392 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Eleanor S. Click [VerfasserIn] |
|---|
| Links: |
doi.org [kostenfrei] |
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| Themen: |
COVID-19 |
|---|
| doi: |
10.3201/eid2902.220712 |
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| funding: |
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|---|---|
| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
DOAJ081608748 |
|---|
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| 520 | |a Ongoing symptoms might follow acute COVID-19. Using electronic health information, we compared pre‒ and post‒COVID-19 diagnostic codes to identify symptoms that had higher encounter incidence in the post‒COVID-19 period as sequelae. This method can be used for hypothesis generation and ongoing monitoring of sequelae of COVID-19 and future emerging diseases. | ||
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| 700 | 0 | |a Adi V. Gundlapalli |e verfasserin |4 aut | |
| 700 | 0 | |a S. Deblina Datta |e verfasserin |4 aut | |
| 700 | 0 | |a Karen K. Wong |e verfasserin |4 aut | |
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