Artificial Intelligence and Machine Learning in Endocrinology and Metabolism: The Dawn of a New Era

Gubbi, Sriram; Hamet, Pavel; Tremblay, Johanne; Koch, Christian A.; Hannah‐Shmouni, Fady

doi:10.3389/fendo.2019.00185

Cited by 40 publications

(25 citation statements)

References 26 publications

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“…Widespread use of ML to improve research in metabolism has led to significant improvements in risk prediction. 21 The use of unsupervised clustering is particularly useful in situations where C-peptide and HbA1c measurements are available for subgroup classification. By developing SNNN algorithms trained on clustered data from ethnically diverse cohorts such as NHANES, one is able to minimize the effect of surrogate measure variability in diabetes subgroup classification that unsupervised clustering would otherwise produce, resulting in profiles that are more reproducible in independent cohorts.…”

Section: Discussionmentioning

confidence: 99%

“…A high prevalence of MS, hypoalphalipoproteinemia and abdominal obesity as well as earlier diabetes onset have previously been reported in Mexicans. 21 23 Mexicans are more susceptible to ectopic and visceral fat accumulation, resulting in an increased cardiometabolic risk profile, which increases the risk of chronic complications, 23 including DKD and NAFLD, both of which are primarily associated with SIDD/SIRD and MOD, respectively. 2 11 Our data show that diabetes subgroup classification could lead to better treatment selection and risk profiling for chronic complications and support the idea that diabetes phenotypes are dynamic and should be reassessed periodically to understand clinical trajectories and reassess the risk of personalized medicine.…”

Section: Discussionmentioning

confidence: 99%

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Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

Bello‐Chavolla

Bahena-López

Vargas‐Vázquez

et al. 2020

BMJ Open Diab Res Care

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IntroductionPrevious reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.Research design and methodsWe trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup.ResultsSNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89).ConclusionsDiabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

Bello‐Chavolla

Bahena-López

Vargas‐Vázquez

et al. 2020

BMJ Open Diab Res Care

View full text Add to dashboard Cite

show abstract

“…These figures, similar to those reported in the latest ALADINO national study, reflect the magnitude of the childhood obesity problem in our society 4 . ML is a suitable approach in predictive analytics, and it has started to be used both for early preventive recommendations related to lifestyle, and to build decision-support tools for disease risk prediction 12,27 . Additionally, in view of the crucial role that prevention plays to control the high obesity prevalence, the identification of its most important risk factors could help to develop effective nutritional and educational intervention strategies.…”

Section: Discussionmentioning

confidence: 99%

Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques

Marcos-Pasero

Colmenarejo

Aguilar-Aguilar

et al. 2021

Sci Rep

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The increased prevalence of childhood obesity is expected to translate in the near future into a concomitant soaring of multiple cardio-metabolic diseases. Obesity has a complex, multifactorial etiology, that includes multiple and multidomain potential risk factors: genetics, dietary and physical activity habits, socio-economic environment, lifestyle, etc. In addition, all these factors are expected to exert their influence through a specific and especially convoluted way during childhood, given the fast growth along this period. Machine Learning methods are the appropriate tools to model this complexity, given their ability to cope with high-dimensional, non-linear data. Here, we have analyzed by Machine Learning a sample of 221 children (6–9 years) from Madrid, Spain. Both Random Forest and Gradient Boosting Machine models have been derived to predict the body mass index from a wide set of 190 multidomain variables (including age, sex, genetic polymorphisms, lifestyle, socio-economic, diet, exercise, and gestation ones). A consensus relative importance of the predictors has been estimated through variable importance measures, implemented robustly through an iterative process that included permutation and multiple imputation. We expect this analysis will help to shed light on the most important variables associated to childhood obesity, in order to choose better treatments for its prevention.

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“…Machine learning is a branch of artificial intelligence (AI), and its statistical learning ability provides important help for the diagnosis, animal population monitoring and analysis of in veterinary epidemiology ( 48 , 49 ). With limited laboratory testing capabilities, AI approaches can rapidly monitor and analyze animal epidemic diseases such as avian influenza, African swine fever (ASF) ( 49 ).…”

Section: Discussionmentioning

confidence: 99%

Prediction for Global Peste des Petits Ruminants Outbreaks Based on a Combination of Random Forest Algorithms and Meteorological Data

et al. 2021

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Peste des Petits Ruminants (PPR) is an acute and highly contagious transboundary disease caused by the PPR virus (PPRV). The virus infects goats, sheep and some wild relatives of small domestic ruminants, such as antelopes. PPR is listed by the World Organization for Animal Health as an animal disease that must be reported promptly. In this paper, PPR outbreak data combined with WorldClim database meteorological data were used to build a PPR prediction model. Using feature selection methods, eight sets of features were selected: bio3, bio10, bio15, bio18, prec7, prec8, prec12, and alt for modeling. Then different machine learning algorithms were used to build models, among which the random forest (RF) algorithm was found to have the best modeling effect. The ACC value of prediction accuracy for the model on the training set can reach 99.10%, while the ACC on the test sets was 99.10%. Therefore, RF algorithms and eight features were finally selected to build the model in order to build the online prediction system. In addition, we adopt single-factor modeling and correlation analysis of modeling variables to explore the impact of each variable on modeling results. It was found that bio18 (the warmest quarterly precipitation), prec7 (the precipitation in July), and prec8 (the precipitation in August) contributed significantly to the model, and the outbreak of the epidemic may have an important relationship with precipitation. Eventually, we used the final qualitative prediction model to establish a global online prediction system for the PPR epidemic.

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Artificial Intelligence and Machine Learning in Endocrinology and Metabolism: The Dawn of a New Era

Cited by 40 publications

References 26 publications

Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques

Prediction for Global Peste des Petits Ruminants Outbreaks Based on a Combination of Random Forest Algorithms and Meteorological Data

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