Artificial intelligence may offer insight into factors determining individual TSH level

Santhanam, Prasanna; Nath, Tanmay; Mohammad, Faiz Khan; Ahima, Rexford S.

doi:10.1371/journal.pone.0233336

Cited by 8 publications

(8 citation statements)

References 21 publications

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“…Such complex relations can best be investigated by taking advantage of flexible ML models. Consequently, advanced ML methods, including RFs, outperform simpler models in predicting TSH, as recently shown by Santhanam et al [ 16 ]. In their study, the best scoring models were RF, gradient boosting and stacking regression with coefficient of determination of R 2 = 0.13 and a mean absolute error of 0.78.…”

Section: Introductionmentioning

confidence: 92%

Analysis of epidemiological association patterns of serum thyrotropin by combining random forests and Bayesian networks

et al. 2022

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Background Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability. Method We here propose a machine learning workflow that combines random forests with Bayesian network surrogate models to allow for a deeper level of interpretation of complex association patterns. We first evaluate the proposed workflow on synthetic data. We then apply it to data from the large population-based Study of Health in Pomerania (SHIP). Based on this combination, we discover and interpret broad patterns of individual serum TSH concentrations, an important marker of thyroid functionality. Results Evaluations using simulated data show that feature associations can be correctly recovered by combining random forests and Bayesian networks. The presented model achieves predictive accuracy that is similar to state-of-the-art models (root mean square error of 0.66, mean absolute error of 0.55, coefficient of determination of R2 = 0.15). We identify 62 relevant features from the final random forest model, ranging from general health variables over dietary and genetic factors to physiological, hematological and hemostasis parameters. The Bayesian network model is used to put these features into context and make the black-box random forest model more understandable. Conclusion We demonstrate that the combination of random forest and Bayesian network analysis is helpful to reveal and interpret broad association patterns of individual TSH concentrations. The discovered patterns are in line with state-of-the-art literature. They may be useful for future thyroid research and improved dosing of therapeutics.

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Section: Introductionmentioning

confidence: 92%

Analysis of epidemiological association patterns of serum thyrotropin by combining random forests and Bayesian networks

et al. 2022

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“…As per our knowledge, our study is the first study that uses body composition variables to predict the exercise capacity in males and females using machine learning. Artificial intelligence offers enormous possibilities in medicine, helping us understand the relationship between biological and metabolic processes and their determinants [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

Body fat predicts exercise capacity in persons with Type 2 Diabetes Mellitus: A machine learning approach

2021

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Diabetes mellitus is associated with increased cardiovascular disease (CVD) related morbidity, mortality and death. Exercise capacity in persons with type 2 diabetes has been shown to be predictive of cardiovascular events. In this study, we used the data from the prospective randomized LOOK AHEAD study and used machine learning algorithms to help predict exercise capacity (measured in Mets) from the baseline data that included cardiovascular history, medications, blood pressure, demographic information, anthropometric and Dual-energy X-Ray Absorptiometry (DXA) measured body composition metrics. We excluded variables with high collinearity and included DXA obtained Subtotal (total minus head) fat percentage and Subtotal lean mass (gms). Thereafter, we used different machine learning methods to predict maximum exercise capacity. The different machine learning models showed a strong predictive performance for both females and males. Our study shows that using baseline data from a large prospective cohort, we can predict maximum exercise capacity in persons with diabetes mellitus. We show that subtotal fat percentage is the most important feature for predicting the exercise capacity for males and females after accounting for other important variables. Until now, BMI and waist circumference were commonly used surrogates for adiposity and there was a relative under-appreciation of body composition metrics for understanding the pathophysiology of CVD. The recognition of body fat percentage as an important marker in determining CVD risk has prognostic implications with respect to cardiovascular morbidity and mortality.

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“…Our analysis was conducted in python version 3.6 ( https://www.python.org ) using the library Scikit Learn. 20 , 21 , 22 Python code for the entire processing pipeline is stored in the GitHub repository ( https://github.com/prasu2172/Albuminuria ).…”

Section: Methodsmentioning

confidence: 99%

Machine learning approach to predicting albuminuria in persons with type 2 diabetes: An analysis of the LOOK AHEAD Cohort

Khitan

Nath

Santhanam

2021

J of Clinical Hypertension

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Albuminuria and estimated glomerular filtration rate (e‐GFR) are early markers of renal disease and cardiovascular outcomes in persons with diabetes. Although body composition has been shown to predict systolic blood pressure, its application in predicting albuminuria is unknown. In this study, we have used machine learning methods to assess the risk of albuminuria in persons with diabetes using body composition and other determinants of metabolic health. This study is a comparative analysis of the different methods to predict albuminuria in persons with diabetes mellitus who are older than 40 years of age, using the LOOK AHEAD study cohort‐baseline characteristics. Age, different metrics of body composition, duration of diabetes, hemoglobin A1c, serum creatinine, serum triglycerides, serum cholesterol, serum HDL, serum LDL, maximum exercise capacity, systolic blood pressure, diastolic blood pressure, and the ankle‐brachial index are used as predictors of albuminuria. We used Area under the curve (AUC) as a metric to compare the classification results of different algorithms, and we show that AUC for the different models are as follows: Random forest classifier‐0.65, gradient boost classifier‐0.61, logistic regression‐0.66, support vector classifier ‐0.61, multilayer perceptron ‐0.67, and stacking classifier‐0.62. We used the Random forest model to show that the duration of diabetes, A1C, serum triglycerides, SBP, Maximum exercise Capacity, serum creatinine, subtotal lean mass, DBP, and subtotal fat mass are important features for the classification of albuminuria. In summary, when applied to metabolic imaging (using DXA), machine learning techniques offer unique insights into the risk factors that determine the development of albuminuria in diabetes.

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Artificial intelligence may offer insight into factors determining individual TSH level

Cited by 8 publications

References 21 publications

Analysis of epidemiological association patterns of serum thyrotropin by combining random forests and Bayesian networks

Analysis of epidemiological association patterns of serum thyrotropin by combining random forests and Bayesian networks

Body fat predicts exercise capacity in persons with Type 2 Diabetes Mellitus: A machine learning approach

Machine learning approach to predicting albuminuria in persons with type 2 diabetes: An analysis of the LOOK AHEAD Cohort

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