Literature DB >> 32325045

Artificial Intelligence: The Future for Diabetes Care.

Samer Ellahham1.   

Abstract

Artificial intelligence (AI) is a fast-growing field and its applications to diabetes, a global pandemic, can reform the approach to diagnosis and management of this chronic condition. Principles of machine learning have been used to build algorithms to support predictive models for the risk of developing diabetes or its consequent complications. Digital therapeutics have proven to be an established intervention for lifestyle therapy in the management of diabetes. Patients are increasingly being empowered for self-management of diabetes, and both patients and health care professionals are benefitting from clinical decision support. AI allows a continuous and burden-free remote monitoring of the patient's symptoms and biomarkers. Further, social media and online communities enhance patient engagement in diabetes care. Technical advances have helped to optimize resource use in diabetes. Together, these intelligent technical reforms have produced better glycemic control with reductions in fasting and postprandial glucose levels, glucose excursions, and glycosylated hemoglobin. AI will introduce a paradigm shift in diabetes care from conventional management strategies to building targeted data-driven precision care.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence (AI); Diabetes; Machine learning; Management; Prediction

Mesh:

Year:  2020        PMID: 32325045     DOI: 10.1016/j.amjmed.2020.03.033

Source DB:  PubMed          Journal:  Am J Med        ISSN: 0002-9343            Impact factor:   4.965


  12 in total

Review 1.  Artificial intelligence perspective in the future of endocrine diseases.

Authors:  Mandana Hasanzad; Bagher Larijani; Hamid Reza Aghaei Meybodi; Negar Sarhangi
Journal:  J Diabetes Metab Disord       Date:  2022-01-11

Review 2.  Artificial intelligence in glomerular diseases.

Authors:  Francesco P Schena; Riccardo Magistroni; Fedelucio Narducci; Daniela I Abbrescia; Vito W Anelli; Tommaso Di Noia
Journal:  Pediatr Nephrol       Date:  2022-03-10       Impact factor: 3.651

3.  Developing Clinical Decision Support System using Machine Learning Methods for Type 2 Diabetes Drug Management.

Authors:  Rajiv Singla; Shivam Aggarwal; Jatin Bindra; Arpan Garg; Ankush Singla
Journal:  Indian J Endocrinol Metab       Date:  2022-04-27

4.  Efficacy of Digitally Supported and Real-Time Self-Monitoring of Blood Glucose-Driven Counseling in Patients with Type 2 Diabetes Mellitus: A Real-World, Retrospective Study in North India.

Authors:  Mudit Sabharwal; Anoop Misra; Amerta Ghosh; Gautam Chopra
Journal:  Diabetes Metab Syndr Obes       Date:  2022-01-05       Impact factor: 3.168

Review 5.  Artificial Intelligence in Current Diabetes Management and Prediction.

Authors:  Akihiro Nomura; Masahiro Noguchi; Mitsuhiro Kometani; Kenji Furukawa; Takashi Yoneda
Journal:  Curr Diab Rep       Date:  2021-12-13       Impact factor: 4.810

Review 6.  A Meta-Analysis of the Effectiveness of Telemedicine in Glycemic Management among Patients with Type 2 Diabetes in Primary Care.

Authors:  Anqi Zhang; Jinsong Wang; Xiaojuan Wan; Ziyi Zhang; Shuhan Zhao; Zihe Guo; Chufan Wang
Journal:  Int J Environ Res Public Health       Date:  2022-03-31       Impact factor: 3.390

7.  A Proposed Multi-Criteria Optimization Approach to Enhance Clinical Outcomes Evaluation for Diabetes Care: A Commentary.

Authors:  Thomas T H Wan; Sarah Matthews; Hsing Luh; Yong Zeng; Zhibo Wang; Lin Yang
Journal:  Health Serv Res Manag Epidemiol       Date:  2022-03-27

8.  Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning Approach.

Authors:  Chia-Lin Lee; Wei-Ju Liu; Shang-Feng Tsai
Journal:  Nutrients       Date:  2022-07-09       Impact factor: 6.706

9.  A pharmacometrician's role in enhancing medication use in pregnancy and lactation.

Authors:  Sara K Quinney; Peter L Bonate
Journal:  J Pharmacokinet Pharmacodyn       Date:  2020-08       Impact factor: 2.745

Review 10.  Machine Learning and Smart Devices for Diabetes Management: Systematic Review.

Authors:  Mohammed Amine Makroum; Mehdi Adda; Abdenour Bouzouane; Hussein Ibrahim
Journal:  Sensors (Basel)       Date:  2022-02-25       Impact factor: 3.576

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