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The primary aim was to develop and validation of perioperative hypoxemia using clinical big data and deep learning technique in pediatric patients
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Retrospective analysis cohort | Other | We analyze pre-existing data base and develop machine-learning-based system that predicts the risk of hypoxemia | ||
| Prospective validation cohort | Other | We validate our model to predict the risk of hypoxemia to prospective cohort |
| Measure | Description | Time Frame |
|---|---|---|
| hypoxemia | pulse oximetry desaturation below 95% | from induction of anesthesia to end of operation, about 3 hours |
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Inclusion Criteria:
Exclusion Criteria:
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pediatric patients
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Hee-Soo Kim, professor | Contact | +82-2-2072-3664 | dami0605@snu.ac.kr |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hee-Soo Kim | Recruiting | Seoul | Soul-t'ukpyolsi | 03080 | South Korea |
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| ID | Term |
|---|---|
| D000860 | Hypoxia |
| ID | Term |
|---|---|
| D012818 | Signs and Symptoms, Respiratory |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
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