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Sa503 MACHINE LEARNING OUTPERFORMS TRADITIONAL RISK MODEL IN IDENTIFYING HIGH-NEED, HIGH-COST PATIENTS WITH INFLAMMATORY BOWEL DISEASES IN A NATIONALLY REPRESENTATIVE COHORT
| Autores principales: | , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
AGA Institute. Published by Elsevier Inc.
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8108304/ http://dx.doi.org/10.1016/S0016-5085(21)01959-4 |
| _version_ | 1783690104444813312 |
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| author | Nguyen, Nghia H. Patel, Sagar Gabunilas, Jason Qian, Alexander Cecil, Alan Ohno-Machado, Lucila Sandborn, William J. Singh, Siddharth Chen, Peter |
| author_facet | Nguyen, Nghia H. Patel, Sagar Gabunilas, Jason Qian, Alexander Cecil, Alan Ohno-Machado, Lucila Sandborn, William J. Singh, Siddharth Chen, Peter |
| author_sort | Nguyen, Nghia H. |
| collection | PubMed |
| description | |
| format | Online Article Text |
| id | pubmed-8108304 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2021 |
| publisher | AGA Institute. Published by Elsevier Inc. |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-81083042021-05-10 Sa503 MACHINE LEARNING OUTPERFORMS TRADITIONAL RISK MODEL IN IDENTIFYING HIGH-NEED, HIGH-COST PATIENTS WITH INFLAMMATORY BOWEL DISEASES IN A NATIONALLY REPRESENTATIVE COHORT Nguyen, Nghia H. Patel, Sagar Gabunilas, Jason Qian, Alexander Cecil, Alan Ohno-Machado, Lucila Sandborn, William J. Singh, Siddharth Chen, Peter Gastroenterology AGA Abstracts AGA Institute. Published by Elsevier Inc. 2021-05 2021-05-10 /pmc/articles/PMC8108304/ http://dx.doi.org/10.1016/S0016-5085(21)01959-4 Text en Copyright © 2021 AGA Institute. Published by Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
| spellingShingle | AGA Abstracts Nguyen, Nghia H. Patel, Sagar Gabunilas, Jason Qian, Alexander Cecil, Alan Ohno-Machado, Lucila Sandborn, William J. Singh, Siddharth Chen, Peter Sa503 MACHINE LEARNING OUTPERFORMS TRADITIONAL RISK MODEL IN IDENTIFYING HIGH-NEED, HIGH-COST PATIENTS WITH INFLAMMATORY BOWEL DISEASES IN A NATIONALLY REPRESENTATIVE COHORT |
| title | Sa503 MACHINE LEARNING OUTPERFORMS TRADITIONAL RISK MODEL IN IDENTIFYING HIGH-NEED, HIGH-COST PATIENTS WITH INFLAMMATORY BOWEL DISEASES IN A NATIONALLY REPRESENTATIVE COHORT |
| title_full | Sa503 MACHINE LEARNING OUTPERFORMS TRADITIONAL RISK MODEL IN IDENTIFYING HIGH-NEED, HIGH-COST PATIENTS WITH INFLAMMATORY BOWEL DISEASES IN A NATIONALLY REPRESENTATIVE COHORT |
| title_fullStr | Sa503 MACHINE LEARNING OUTPERFORMS TRADITIONAL RISK MODEL IN IDENTIFYING HIGH-NEED, HIGH-COST PATIENTS WITH INFLAMMATORY BOWEL DISEASES IN A NATIONALLY REPRESENTATIVE COHORT |
| title_full_unstemmed | Sa503 MACHINE LEARNING OUTPERFORMS TRADITIONAL RISK MODEL IN IDENTIFYING HIGH-NEED, HIGH-COST PATIENTS WITH INFLAMMATORY BOWEL DISEASES IN A NATIONALLY REPRESENTATIVE COHORT |
| title_short | Sa503 MACHINE LEARNING OUTPERFORMS TRADITIONAL RISK MODEL IN IDENTIFYING HIGH-NEED, HIGH-COST PATIENTS WITH INFLAMMATORY BOWEL DISEASES IN A NATIONALLY REPRESENTATIVE COHORT |
| title_sort | sa503 machine learning outperforms traditional risk model in identifying high-need, high-cost patients with inflammatory bowel diseases in a nationally representative cohort |
| topic | AGA Abstracts |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8108304/ http://dx.doi.org/10.1016/S0016-5085(21)01959-4 |
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