Malnutrition, Health and the Role of Machine Learning in Clinical Setting
| Autor: | Vishakha Sharma, Matthew D. Schoenholtz, Josep Bassaganya-Riera, Ayesha Khan, Vaibhav Sharma, David J. Wassmer, Raquel Hontecillas, Ramin Zand, Vida Abedi |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Decision support system Hospitalized patients Endocrinology Diabetes and Metabolism MEDLINE 030209 endocrinology & metabolism lcsh:TX341-641 Review malnutrition Health records Machine learning computer.software_genre 03 medical and health sciences 0302 clinical medicine Medicine nutrition assessment tools Nutritional deficiency Nutrition 030109 nutrition & dietetics Nutrition and Dietetics business.industry medicine.disease artificial intelligence Malnutrition machine learning Malnutrition screening Healthcare settings ASPEN Artificial intelligence business computer lcsh:Nutrition. Foods and food supply Food Science |
| Zdroj: | Frontiers in Nutrition, Vol 7 (2020) Frontiers in Nutrition |
| DOI: | 10.3389/fnut.2020.00044/full |
| Popis: | Nutrition plays a vital role in health and the recovery process. Deficiencies in macronutrients and micronutrients can impact the development and progression of various disorders. However, malnutrition screening tools and their utility in the clinical setting remain largely understudied. In this study, we summarize the importance of nutritional adequacy and its association with neurological, cardiovascular, and immune-related disorders. We also examine general and specific malnutrition assessment tools utilized in healthcare settings. Since the implementation of the screening process in 2016, malnutrition data from hospitalized patients in the Geisinger Health System is presented and discussed as a case study. Clinical data from five Geisinger hospitals shows that approximately 10% of all admitted patients are acknowledged for having some form of nutritional deficiency, from which about 60-80% of the patients are targeted for a more comprehensive assessment. Finally, we conclude that with a reflection on how technological advances, specifically machine learning-based algorithms, can be integrated into electronic health records to provide decision support system to care providers in the identification and management of patients at higher risk of malnutrition. |
| Databáze: | OpenAIRE |
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