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Cited by 131Diabetes Management and ResearchArtificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Artificial Intelligence Methodologies and Their Application to Diabetes

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Rigla, Mercedes, García-Sáez, Gema, Pons, Belén, Hernando, Maria Elena·2018·Journal of Diabetes Science and Technology
DOI 10.1177/1932296817710475PMC PMC5851211
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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.

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  • The algorithms used in closed-loop systems primarily include model predictive control, proportional-integral-derivative, fuzzy logic, and learning algorithms.

    “The algorithms used in closed-loop systems primarily include model predictive control, proportional-integral-derivative, fuzzy logic, and learning algorithms.86”

    From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [86]

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  1. [2]2. Ramesh AN, Kambhampati C, Monson JR, Drew PJ. Artificial intelligence in medicine. Ann R Coll Surg Engl. 2004;86(5):334-338. Go to Reference Crossref PubMed Web of Science Google Scholar
  2. [3]3. Mitchell T. Machine Learning. New York, NY: McGraw-Hill Education; 1997. Go to Reference Google Scholar
  3. [4]4. Holland J. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Ann Arbor: University of Michigan Press; 1975. Go to Reference Google Scholar
  4. [5]5. Quinlan JR. Discovering rules by induction from large collections of examples. In: Michie D, ed. Expert Systems in the Micro Electronic Age. Edinburgh, UK: Edinburgh University Press; 1979:168-201. Go to Reference Google Scholar
  5. [6]6. Quinlan J. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann; 1993. Go to Reference Crossref Google Scholar
  6. [7]7. Usher D, Dumskyj M, Himaga M, Williamson TH, Nussey S, Boyce J. Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet Med. 2004;21(1):84-90. Crossref PubMed Web of Science Google Scholar a [...] tasks such as screening in type 2 diabetes b [...] Diabet Med
  7. [8]8. Caballero-Ruiz E, García-Sáez G, Rigla M, Villaplana M, Pons B, Hernando M. Automatic classification of glycaemia measurements to enhance data interpretation in an expert system for gestational diabetes. Expert Syst Appl. 2016;63:386-396. Crossref Web of Science Google Scholar a [...] and blood glucose classification. b [...] Expert systems with applications
  8. [9]9. Yu W, Liu T, Valdez R, Gwinn M, Khoury MJ. Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC Med Inform Decis Mak. 2010;10(1):16. Crossref PubMed Web of Science Google Scholar a [...] to predict prediabetes and diabetes disease b [...] BMC Medical Informatics and Decision Making
  9. [10]10. Kumari V, Chitra R. Classification of diabetes disease using support vector machine. Int J Eng Res Appl. 2013;3(2):1797-1801. Google Scholar a [...] and in diabetes diagnosis. b [...] Int J Eng Research and App
  10. [12]12. Nimri R, Bratina N, Kordonouri O, et al. MD-Logic overnight type 1 diabetes control in home settings: a multicentre, multinational, single blind randomized trial. Diabetes Obes Metab. 2017;19(4):553-561. Crossref PubMed Web of Science Google Scholar a [...] studies, even in the ambulatory setting. b [...] Diabetes Obes Metab
  11. [13]13. Mauseth R, Wang Y, Dassau E, et al. Proposed clinical application for tuning fuzzy logic controller of artificial pancreas utilizing a personalization factor. J Diabetes Sci Technol. 2010;4(4):913-922. Go to Reference Crossref PubMed Google Scholar
  12. [14]14. Mauseth R, Hirsch IB, Bollyky J, et al. Use of a “fuzzy logic” controller in a closed-loop artificial pancreas. Diabetes Technol Ther. 2013;15(8):628-633. Crossref PubMed Web of Science Google Scholar a [...] tested in an pilot study with good results. b [...] Diabetes Technol Ther
  13. [16]16. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402-2410. Crossref PubMed Web of Science Google Scholar a [...] with high sensitivity and high specificity. b [...] JAMA
  14. [17]17. Heinemann L. The diabetes technologist: a practical solution in dealing with technology in everyday practice? J Diabetes Sci Technol. 2012;6(6):1240-1241. Go to Reference Crossref PubMed Google Scholar
  15. [18]18. Montani S, Magni P, Bellazzi R, Larizza C, Roudsari AV, Carson ER. Integrating model-based decision support in a multi-modal reasoning system for managing type 1 diabetic patients. Artif Intell Med. 2003;29(1-2):131-151. Go to Reference Crossref PubMed Web of Science Google Scholar
  16. [19]19. Capel I, Rigla M, García-Sáez G, et al. Artificial pancreas using a personalized rule-based controller achieves overnight normoglycemia in patients with type 1 diabetes. Diabetes Technol Ther. 2014;16(3):172-179. Crossref PubMed Web of Science Google Scholar a [...] Diabetes Technol Ther b [...] Diabetes Technol Ther
  17. [20]20. Hidalgo JI, Maqueda E, Risco-Martín JL, Cuesta-Infante A, Colmenar JM, Nobel J. glUCModel: a monitoring and modeling system for chronic diseases applied to diabetes. J Biomed Inform. 2014;48:183-192. Go to Reference Crossref PubMed Web of Science Google Scholar
  18. [21]21. Lu X, Huang Z, Duan H. Supporting adaptive clinical treatment processes through recommendations. Comput Methods Programs Biomed. 2012;107(3):413-424. Go to Reference Crossref PubMed Web of Science Google Scholar
  19. [22]22. Schwartz FL, Vernier SJ, Shubrook JH, Marling CR. Evaluating the automated blood glucose pattern detection and case-retrieval modules of the 4 Diabetes Support System. J Diabetes Sci Technol. 2010;4(6):1563-1569. Go to Reference Crossref PubMed Google Scholar
  20. [23]23. Schwartz FL, Shubrook JH, Marling CR. Use of case-based reasoning to enhance intensive management of patients on insulin pump therapy. J Diabetes Sci Technol. 2008;2(4):603-611. Go to Reference Crossref PubMed Google Scholar
  21. [24]24. Armengol E, Palaudàries A, Plaza E. Individual prognosis of diabetes long-term risks: a CBR approach. Methods Inf Med. 2001;40(1):46-51. Go to Reference Crossref PubMed Web of Science Google Scholar
  22. [25]25. El-Sappagh S, Elmogy M, Riad AM. A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis. Artif Intell Med. 2015;65(3):179-208. Go to Reference Crossref PubMed Web of Science Google Scholar
  23. [26]26. Mauseth R, Lord SM, Hirsch IB, Kircher RC, Matheson DP, Greenbaum CJ. Stress testing of an artificial pancreas system with pizza and exercise leads to improvements in the system’s fuzzy logic controller. J Diabetes Sci Technol. 2015;9(6):1253-1259. Go to Reference Crossref PubMed Google Scholar
  24. [27]27. Phillip M, Battelino T, Atlas E, et al. Nocturnal glucose control with an artificial pancreas at a diabetes camp. N Engl J Med. 2013;368(9):824-833. Go to Reference Crossref PubMed Web of Science Google Scholar
  25. [28]28. Fereydouneyan F, Zare A, Mehrshad N. Using a fuzzy controller optimized by a genetic algorithm to regulate blood glucose level in type 1 diabetes. J Med Eng Technol. 2011;35(5):224-230. Go to Reference Crossref PubMed Google Scholar
  26. [29]29. Sacco IC, Hamamoto AN, Tonicelli LM, Watari R, Ortega NR, Sartor CD. Abnormalities of plantar pressure distribution in early, intermediate, and late stages of diabetic neuropathy. Gait Posture. 2014;40(4):570-574. Go to Reference Crossref PubMed Web of Science Google Scholar
  27. [30]30. Marateb HR, Mansourian M, Faghihimani E, Amini M, Farina D. A hybrid intelligent system for diagnosing microalbuminuria in type 2 diabetes patients without having to measure urinary albumin. Comput Biol Med. 2014;45:34-42. Go to Reference Crossref PubMed Web of Science Google Scholar
  28. [31]31. Beloufa F, Chikh MA. Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm. Comput Methods Programs Biomed. 2013;112(1):92-103. Go to Reference Crossref PubMed Web of Science Google Scholar
  29. [32]32. Settouti N, Chikh MA, Saidi M. Generating fuzzy rules for constructing interpretable classifier of diabetes disease. Australas Phys Eng Sci Med. 2012;35(3):257-270. Go to Reference Crossref PubMed Web of Science Google Scholar
  30. [33]33. Ling SH, Nguyen HT. Natural occurrence of nocturnal hypoglycemia detection using hybrid particle swarm optimized fuzzy reasoning model. Artif Intell Med. 2012;55(3):177-184. Go to Reference Crossref PubMed Web of Science Google Scholar
  31. [34]34. Lee CS, Wang MH. A fuzzy expert system for diabetes decision support application. IEEE Trans Syst Man Cybern B Cybern. 2011;41(1):139-153. Go to Reference Crossref PubMed Web of Science Google Scholar
  32. [35]35. Campos-Delgado DU, Hernández-Ordoñez M, Femat R, Gordillo-Moscoso A. Fuzzy-based controller for glucose regulation in type-1 diabetic patients by subcutaneous route. IEEE Trans Biomed Eng. 2006;53(11):2201-2210. Go to Reference Crossref PubMed Web of Science Google Scholar
  33. [36]36. García-Sáez G, Alonso JM, Molero J, et al. Mealtime blood glucose classifier based on fuzzy logic for the Diabtel Telemedicine System. In: Combi C, Shahar Y, Abu-Hanna A, eds. AIME. Berlin, Germany: Springer; 2009:295-304. Go to Reference Google Scholar
  34. [37]37. Norouzi J, Yadollahpour A, Mirbagheri SA, Mazdeh MM, Hosseini SA. Predicting renal failure progression in chronic kidney disease using integrated intelligent fuzzy expert system. Comput Math Methods Med. 2016;2016(3):6080814. Go to Reference PubMed Web of Science Google Scholar
  35. [38]38. Ibrahim S, Chowriappa P, Dua S, et al. Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier. Med Biol Eng Comput. 2015;53(12):1345-1360. Go to Reference Crossref PubMed Web of Science Google Scholar
  36. [39]39. Zarkogianni K, Mitsis K, Litsa E, et al. Comparative assessment of glucose prediction models for patients with type 1 diabetes mellitus applying sensors for glucose and physical activity monitoring. Med Biol Eng Comput. 2015;53(12):1333-1343. Go to Reference Crossref PubMed Web of Science Google Scholar
  37. [40]40. San PP, Ling SH, Nguyen HT. Intelligent detection of hypoglycemic episodes in children with type 1 diabetes using adaptive neural-fuzzy inference system. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:6325-6328. Go to Reference PubMed Web of Science Google Scholar
  38. [41]41. Rau HH, Hsu CY, Lin YA, et al. Development of a web-based liver cancer prediction model for type II diabetes patients by using an artificial neural network. Comput Methods Programs Biomed. 2016;125:58-65. Go to Reference Crossref PubMed Web of Science Google Scholar
  39. [42]42. Chen J, Tang H, Huang H, et al. Development and validation of new glomerular filtration rate predicting models for Chinese patients with type 2 diabetes. J Transl Med. 2015;13:317. Go to Reference Crossref PubMed Web of Science Google Scholar
  40. [43]43. Choi SB, Kim WJ, Yoo TK, et al. Screening for prediabetes using machine learning models. Comput Math Methods Med. 2014;2014:618976. Go to Reference Crossref PubMed Web of Science Google Scholar
  41. [44]44. Saraoğlu HM, Temurtas F, Altıkat S. Quantitative classification of HbA1C and blood glucose level for diabetes diagnosis using neural networks. Australas Phys Eng Sci Med. 2013;36(4):397-403. Go to Reference Crossref PubMed Web of Science Google Scholar
  42. [45]45. Wang C, Li L, Wang L, et al. Evaluating the risk of type 2 diabetes mellitus using artificial neural network: an effective classification approach. Diabetes Res Clin Pract. 2013;100(1):111-118. Go to Reference Crossref PubMed Web of Science Google Scholar
  43. [46]46. Shi H, Lu Y, Du J, et al. Application of back propagation artificial neural network on genetic variants in adiponectin ADIPOQ, peroxisome proliferator-activated receptor-γ, and retinoid X receptor-α genes and type 2 diabetes risk in a Chinese Han population. Diabetes Technol Ther. 2012;14(3):293-300. Go to Reference Crossref PubMed Web of Science Google Scholar
  44. [47]47. Lee YC, Lee WJ, Liew PL. Predictors of remission of type 2 diabetes mellitus in obese patients after gastrointestinal surgery. Obes Res Clin Pract. 2013;7(6):e494-e500. Go to Reference Crossref PubMed Web of Science Google Scholar
  45. [48]48. Franklin SW, Rajan SE. An automated retinal imaging method for the early diagnosis of diabetic retinopathy. Technol Health Care. 2013;21(6):557-569. Go to Reference Crossref PubMed Web of Science Google Scholar
  46. [49]49. Nayak J, Bhat PS, Acharya UR. Automatic identification of diabetic maculopathy stages using fundus images. J Med Eng Technol. 2009;33(2):119-129. Go to Reference Crossref PubMed Google Scholar
  47. [50]50. Nayak J, Bhat PS, Acharya R, Lim CM, Kagathi M. Automated identification of diabetic retinopathy stages using digital fundus images. J Med Syst. 2008;32(2):107-115. Go to Reference Crossref PubMed Web of Science Google Scholar
  48. [51]51. Singh K, Singh VK, Agrawal NK, Gupta SK, Singh K. Association of Toll-like receptor 4 polymorphisms with diabetic foot ulcers and application of artificial neural network in DFU risk assessment in type 2 diabetes patients. Biomed Res Int. 2013;2013:318686. Go to Reference Crossref PubMed Web of Science Google Scholar
  49. [52]52. Perez-Gandia C, Facchinetti A, Sparacino G, et al. Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring. Diabetes Technol Ther. 2010;12(1):81-88. Go to Reference Crossref PubMed Web of Science Google Scholar
  50. [53]53. Eller-Vainicher C, Zhukouskaya VV, Tolkachev YV, et al. Low bone mineral density and its predictors in type 1 diabetic patients evaluated by the classic statistics and artificial neural network analysis. Diabetes Care. 2011;34(10):2186-2191. Go to Reference Crossref PubMed Web of Science Google Scholar
  51. [54]54. Welikala RA, Fraz MM, Dehmeshki J, et al. Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy. Comput Med Imaging Graph. 2015;43:64-77. Go to Reference Crossref PubMed Web of Science Google Scholar
  52. [55]55. Ganesan K, Martis RJ, Acharya UR, et al. Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images. Med Biol Eng Comput. 2014;52(8):663-672. Go to Reference Crossref PubMed Web of Science Google Scholar
  53. [56]56. Quellec G, Lamard M, Josselin PM, Cazuguel G, Cochener B, Roux C. Optimal wavelet transform for the detection of microaneurysms in retina photographs. IEEE Trans Med Imaging. 2008;27(9):1230-1241. Go to Reference Crossref PubMed Web of Science Google Scholar
  54. [57]57. Gyuk P, Szabo I, Vassanyi I, Kosa I, Kovacs L. Combined model for diabetes lifestyle support. Stud Health Technol Inform. 2014;197:77-81. Go to Reference PubMed Web of Science Google Scholar
  55. [58]58. Greenwood NJ, Gunton JE. A computational proof of concept of a machine-intelligent artificial pancreas using Lyapunov stability and differential game theory. J Diabetes Sci Technol. 2014;8(4):791-806. Go to Reference Crossref PubMed Google Scholar
  56. [59]59. Ghosh S, Gude S. A genetic algorithm tuned optimal controller for glucose regulation in type 1 diabetic subjects. Int J Numer Method Biomed Eng. 2012;28(8):877-889. Go to Reference Crossref PubMed Web of Science Google Scholar
  57. [60]60. Morbiducci U, Di Benedetto G, Kautzky-Willer A, Deriu MA, Pacini G, Tura A. Identification of a model of non-esterified fatty acids dynamics through genetic algorithms: the case of women with a history of gestational diabetes. Comput Biol Med. 2011;41(3):146-153. Go to Reference Crossref PubMed Web of Science Google Scholar
  58. [61]61. Morbiducci U, Di Benedetto G, Kautzky-Willer A, Pacini G, Tura A. Improved usability of the minimal model of insulin sensitivity based on an automated approach and genetic algorithms for parameter estimation. Clin Sci (Lond). 2007;112(4):257-263. Go to Reference Crossref PubMed Web of Science Google Scholar
  59. [62]62. Boisvert MR, Koski KG, Burns DH, Skinner CD. Early prediction of macrosomia based on an analysis of second trimester amniotic fluid by capillary electrophoresis. Biomark Med. 2012;6(5):655-662. Go to Reference Crossref PubMed Web of Science Google Scholar
  60. [63]63. Boisvert MR, Koski KG, Burns DH, Skinner CD. Prediction of gestational diabetes mellitus based on an analysis of amniotic fluid by capillary electrophoresis. Biomark Med. 2012;6(5):645-653. Go to Reference Crossref PubMed Web of Science Google Scholar
  61. [64]64. Kaabouch N, Hu WC, Chen Y, Anderson JW, Ames F, Paulson R. Predicting neuropathic ulceration: analysis of static temperature distributions in thermal images. J Biomed Opt. 2010;15(6):061715. Go to Reference Crossref PubMed Web of Science Google Scholar
  62. [65]65. Miller RG, Anderson SJ, Costacou T, Sekikawa A, Orchard TJ. Risk stratification for 25-year cardiovascular disease incidence in type 1 diabetes: tree-structured survival analysis of the Pittsburgh Epidemiology of Diabetes Complications study. Diab Vasc Dis Res. 2016;13(4):250-259. Go to Reference Crossref PubMed Web of Science Google Scholar
  63. [66]66. Karaolis MA, Moutiris JA, Hadjipanayi D, Pattichis CS. Assessment of the risk factors of coronary heart events based on data mining with decision trees. IEEE Trans Inf Technol Biomed. 2010;14(3):559-566. Go to Reference Crossref PubMed Web of Science Google Scholar
  64. [67]67. Leontidis G, Al-Diri B, Wigdahl J, Hunter A. Evaluation of geometric features as biomarkers of diabetic retinopathy for characterizing the retinal vascular changes during the progression of diabetes. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:5255-5259. Go to Reference PubMed Web of Science Google Scholar
  65. [68]68. Koprowski R, Teper S, Wróbel Z, Wylegala E. Automatic analysis of selected choroidal diseases in OCT images of the eye fundus. Biomed Eng Online. 2013;12:117. Go to Reference Crossref PubMed Web of Science Google Scholar
  66. [69]69. Stranieri A, Abawajy J, Kelarev A, Huda S, Chowdhury M, Jelinek HF. An approach for Ewing test selection to support the clinical assessment of cardiac autonomic neuropathy. Artif Intell Med. 2013;58(3):185-193. Go to Reference Crossref PubMed Web of Science Google Scholar
  67. [70]70. Li CP, Zhi XY, Ma J, et al. Performance comparison between Logistic regression, decision trees, and multilayer perceptron in predicting peripheral neuropathy in type 2 diabetes mellitus. Chin Med J (Engl). 2012;125(5):851-857. Go to Reference PubMed Web of Science Google Scholar

Cited by

Sorted by citations

Authors
Rigla, M., García-Sáez, G., Pons, B., & Hernando, M. E.
Affiliations
  • Corporació Sanitària Parc Taulí (ES)
  • Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (ES)
Corresponding author
Rigla, Mercedes
ORCID
Venue
Journal of Diabetes Science and Technology
Publisher
SAGE Publications
Published
2017-05-25
Volume
12
Issue
2
Pages
303-310
ISSN
1932-2968 (ISSN-L)
DOI
10.1177/1932296817710475
PMC
PMC5851211
OpenAlex
W2616911995
Cited by
131
References
1
Citations by year
2026: 12 · 2025: 12 · 2024: 12 · 2023: 20 · 2022: 18 · 2021: 16 · 2020: 22 · 2019: 9 · 2018: 8 · 2017: 1
FWCI
8.65
Type
journal article
Language
en
Open access
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
  • Journal of Diabetes Science and Technology (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Hispana (acceptedVersion, cc-by-nc-nd)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-nd)
  • Archivo Digital UPM (Universidad Politécnica de Madrid) (submittedVersion, cc-by-nc-nd)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
artificial intelligence, diabetes management, continuous glucose monitoring, insulin pump, wearable devices, decision support, knowledge acquisition, clinical decision support, interdisciplinary research
MeSH terms
Machine Learning, Artificial Intelligence, Diabetes Mellitus, Humans, Decision Support Systems, Clinical

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