Development and Validation of a Machine Learning System to Identify Reflux Events in Esophageal 24-Hour pH/Impedance Studies.
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| Title: | Development and Validation of a Machine Learning System to Identify Reflux Events in Esophageal 24-Hour pH/Impedance Studies. |
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| Authors: | Zhou MJ; Division of Gastroenterology and Hepatology, Stanford University, Stanford, California, USA., Zikos T; Kaiser Foundation Hospitals, Pasadena, California, USA., Goel K; Department of Computer Science, Stanford University, Stanford, California, USA., Goel K; University of California Berkeley College of Engineering, Berkeley, California, USA., Gu A; Department of Computer Science, Stanford University, Stanford, California, USA., Re C; Department of Computer Science, Stanford University, Stanford, California, USA., Florez Rodriguez DJ; Division of Gastroenterology and Hepatology, Stanford University, Stanford, California, USA., Clarke JO; Division of Gastroenterology and Hepatology, Stanford University, Stanford, California, USA., Garcia P; Division of Gastroenterology and Hepatology, Stanford University, Stanford, California, USA., Fernandez-Becker N; Division of Gastroenterology and Hepatology, Stanford University, Stanford, California, USA., Sonu I; Division of Gastroenterology and Hepatology, Stanford University, Stanford, California, USA., Kamal A; Division of Gastroenterology and Hepatology, Stanford University, Stanford, California, USA., Sinha SR; Division of Gastroenterology and Hepatology, Stanford University, Stanford, California, USA. |
| Source: | Clinical and translational gastroenterology [Clin Transl Gastroenterol] 2023 Oct 01; Vol. 14 (10), pp. e00634. Date of Electronic Publication: 2023 Oct 01. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Wolters Kluwer Health Country of Publication: United States NLM ID: 101532142 Publication Model: Electronic Cited Medium: Internet ISSN: 2155-384X (Electronic) Linking ISSN: 2155384X NLM ISO Abbreviation: Clin Transl Gastroenterol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2155-384X |
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| DOI: | 10.14309/ctg.0000000000000634 |
