Assessing the Utility of a Machine-Learning Model to Assist With the Assignment of the American Society of Anesthesiology Physical Status Classification in Pediatric Patients.
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| Title: | Assessing the Utility of a Machine-Learning Model to Assist With the Assignment of the American Society of Anesthesiology Physical Status Classification in Pediatric Patients. |
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| Authors: | Ferrari LR; From the Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital.; Harvard Medical School., Leahy I; From the Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital., Staffa SJ; From the Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital., Hong P; Harvard Medical School.; Data and Analytics Services, Information Technology, Boston Children's Hospital.; Complex Care Service, Division of General Pediatrics, Boston Children's Hospital., Stringfellow I; Complex Care Service, Division of General Pediatrics, Boston Children's Hospital., Berry JG; Harvard Medical School.; Complex Care Service, Division of General Pediatrics, Boston Children's Hospital. |
| Source: | Anesthesia and analgesia [Anesth Analg] 2024 Nov 01; Vol. 139 (5), pp. 1017-1026. Date of Electronic Publication: 2023 Dec 13. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 1310650 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1526-7598 (Electronic) Linking ISSN: 00032999 NLM ISO Abbreviation: Anesth Analg Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 1526-7598 |
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| DOI: | 10.1213/ANE.0000000000006761 |
