Literature DB >> 28539087

Artificial Intelligence Methodologies and Their Application to Diabetes.

Mercedes Rigla1, Gema García-Sáez2,3, Belén Pons1, Maria Elena Hernando2,3.   

Abstract

In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. All these parameters, when analyzed, can be helpful for patients and doctors' decision support. Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition. Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic. AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans. Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes. In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers-doctors and nurses-in this field.

Entities:  

Keywords:  artificial intelligence; decision support; diabetes; machine learning

Mesh:

Year:  2017        PMID: 28539087      PMCID: PMC5851211          DOI: 10.1177/1932296817710475

Source DB:  PubMed          Journal:  J Diabetes Sci Technol        ISSN: 1932-2968


  63 in total

1.  Assessment of the risk factors of coronary heart events based on data mining with decision trees.

Authors:  Minas A Karaolis; Joseph A Moutiris; Demetra Hadjipanayi; Constantinos S Pattichis
Journal:  IEEE Trans Inf Technol Biomed       Date:  2010-01-12

2.  Evaluating the risk of type 2 diabetes mellitus using artificial neural network: an effective classification approach.

Authors:  Chongjian Wang; Linlin Li; Ling Wang; Zhiguang Ping; Muanda Tsobo Flory; Gaoshuai Wang; Yuanlin Xi; Wenjie Li
Journal:  Diabetes Res Clin Pract       Date:  2013-02-28       Impact factor: 5.602

3.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

Authors:  Varun Gulshan; Lily Peng; Marc Coram; Martin C Stumpe; Derek Wu; Arunachalam Narayanaswamy; Subhashini Venugopalan; Kasumi Widner; Tom Madams; Jorge Cuadros; Ramasamy Kim; Rajiv Raman; Philip C Nelson; Jessica L Mega; Dale R Webster
Journal:  JAMA       Date:  2016-12-13       Impact factor: 56.272

4.  Predicting neuropathic ulceration: analysis of static temperature distributions in thermal images.

Authors:  Naima Kaabouch; Wen-Chen Hu; Yi Chen; Julie W Anderson; Forrest Ames; Rolf Paulson
Journal:  J Biomed Opt       Date:  2010 Nov-Dec       Impact factor: 3.170

5.  An automated retinal imaging method for the early diagnosis of diabetic retinopathy.

Authors:  S Wilfred Franklin; S Edward Rajan
Journal:  Technol Health Care       Date:  2013       Impact factor: 1.285

6.  Fuzzy-based controller for glucose regulation in type-1 diabetic patients by subcutaneous route.

Authors:  D U Campos-Delgado; M Hernández-Ordoñez; R Femat; A Gordillo-Moscoso
Journal:  IEEE Trans Biomed Eng       Date:  2006-11       Impact factor: 4.538

7.  Combined model for diabetes lifestyle support.

Authors:  Peter Gyuk; Istvan Szabo; Istvan Vassanyi; Istvan Kosa; Levente Kovacs
Journal:  Stud Health Technol Inform       Date:  2014

8.  Predictors of remission of type 2 diabetes mellitus in obese patients after gastrointestinal surgery.

Authors:  Yi-Chih Lee; Wei-Jei Lee; Phui-Ly Liew
Journal:  Obes Res Clin Pract       Date:  2013-12       Impact factor: 2.288

9.  Screening for prediabetes using machine learning models.

Authors:  Soo Beom Choi; Won Jae Kim; Tae Keun Yoo; Jee Soo Park; Jai Won Chung; Yong-ho Lee; Eun Seok Kang; Deok Won Kim
Journal:  Comput Math Methods Med       Date:  2014-07-16       Impact factor: 2.238

10.  Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System.

Authors:  Jamshid Norouzi; Ali Yadollahpour; Seyed Ahmad Mirbagheri; Mitra Mahdavi Mazdeh; Seyed Ahmad Hosseini
Journal:  Comput Math Methods Med       Date:  2016-02-02       Impact factor: 2.238

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  20 in total

1.  Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy.

Authors:  Gregory P Forlenza
Journal:  Diabetes Technol Ther       Date:  2019-06       Impact factor: 6.118

2.  Decision Support in Diabetes Care: The Challenge of Supporting Patients in Their Daily Living Using a Mobile Glucose Predictor.

Authors:  Carmen Pérez-Gandía; Gema García-Sáez; David Subías; Agustín Rodríguez-Herrero; Enrique J Gómez; Mercedes Rigla; M Elena Hernando
Journal:  J Diabetes Sci Technol       Date:  2018-03

3.  Continuous Glucose Monitoring: Current Use in Diabetes Management and Possible Future Applications.

Authors:  Martina Vettoretti; Giacomo Cappon; Giada Acciaroli; Andrea Facchinetti; Giovanni Sparacino
Journal:  J Diabetes Sci Technol       Date:  2018-05-22

Review 4.  Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades.

Authors:  Samir Malakar; Soumya Deep Roy; Soham Das; Swaraj Sen; Juan D Velásquez; Ram Sarkar
Journal:  Arch Comput Methods Eng       Date:  2022-06-15       Impact factor: 8.171

5.  Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques.

Authors:  Qing Liu; Miao Zhang; Yifeng He; Lei Zhang; Jingui Zou; Yaqiong Yan; Yan Guo
Journal:  J Pers Med       Date:  2022-05-31

6.  A multi-class classification model for supporting the diagnosis of type II diabetes mellitus.

Authors:  Kuang-Ming Kuo; Paul Talley; YuHsi Kao; Chi Hsien Huang
Journal:  PeerJ       Date:  2020-09-10       Impact factor: 2.984

7.  Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus.

Authors:  Shinji Tarumi; Wataru Takeuchi; George Chalkidis; Salvador Rodriguez-Loya; Junichi Kuwata; Michael Flynn; Kyle M Turner; Farrant H Sakaguchi; Charlene Weir; Heidi Kramer; David E Shields; Phillip B Warner; Polina Kukhareva; Hideyuki Ban; Kensaku Kawamoto
Journal:  Methods Inf Med       Date:  2021-05-11       Impact factor: 2.176

8.  Predicting Mortality in Diabetic ICU Patients Using Machine Learning and Severity Indices.

Authors:  Rajsavi S Anand; Paul Stey; Sukrit Jain; Dustin R Biron; Harikrishna Bhatt; Kristina Monteiro; Edward Feller; Megan L Ranney; Indra Neil Sarkar; Elizabeth S Chen
Journal:  AMIA Jt Summits Transl Sci Proc       Date:  2018-05-18

Review 9.  Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.

Authors:  Ivan Contreras; Josep Vehi
Journal:  J Med Internet Res       Date:  2018-05-30       Impact factor: 5.428

10.  mHealth for diabetes self-management in the Kingdom of Saudi Arabia: barriers and solutions.

Authors:  Turki Alanzi
Journal:  J Multidiscip Healthc       Date:  2018-10-08
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