From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations-Reference-Cited by-同舟云学术

From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations

Published:2021-05-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
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Author:

G. Mitchell Elliot¹,M. Heitkemper Elizabeth²,Burgermaster Marissa³,E. Levine Matthew⁴,Miao Yishen⁵,L. Hwang Maria⁶,M. Desai Pooja¹,Cassells Andrea⁷,N. Tobin Jonathan⁸,G. Tabak Esteban⁹,J. Albers David¹⁰,M. Smaldone Arlene¹¹,Mamykina Lena¹

Affiliation:

1. Department of Biomedical Informatics, Columbia University, United States

2. School of Nursing, The University of Texas at Austin, United States

3. Department of Population Health, Dell Medical School and Department of Nutritional Sciences, The University of Texas at Austin, United States

4. Department of Computing and Mathematical Sciences, California Institute of Technology, United States

5. Department of Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, United States

6. Science and Math, Fashion Institute of Technology, United States

7. Clinical Directors Network (CDN), United States

8. Clinical Directors Network (CDN) and The Rockefeller University, United States

9. Courant Institute of Mathematical Sciences, United States

10. University of Colorado, Anschutz Medical Campus, Section of Informatics and Data Science, Departments of Pediatrics, Biomedical Engineering, and Biostatistics and Informatics and Department of Biomedical Informatics, Columbia University, United States

11. School of Nursing, Columbia University, United States

Funder

National Institute of Diabetes and Digestive and Kidney Diseases

National Library of Medicine

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3411764.3445555

Reference104 articles.

1. Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype

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4. New IDF clinical practice recommendations for managing type 2 diabetes in primary care

5. Reviewing reflection

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