TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records

Bernardini, Michele; Morettini, Micaela; Romeo, Luca; Frontoni, Emanuele; Burattini, Laura

doi:10.1016/j.compbiomed.2019.103358

Cited by 25 publications

(16 citation statements)

References 33 publications

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“…bias into the maximum likelihood estimation; the inclusion of this bias helps to reduce the variance, thus improving the predictions for new subjects (or the generalization of results) (Hastie, 2017). Machine-learning techniques have been extensively explored in recent years for the prevention and management of T2DM (Huang et al, 2007;Yu et al, 2010;Perveen et al, 2016;Dalakleidi et al, 2017;Maniruzzaman et al, 2017;Zheng and Zhang, 2017;Talaei-Khoei and Wilson, 2018;Bernardini et al, 2019Bernardini et al, , 2020 but also showing possible criticalities. In fact, very often, the analysis with these techniques on large amounts of heterogeneous data leads to identify spurious correlations (Rumbold et al, 2020), indicating that the creation of appropriate databases, with selected groups of subjects and characteristics, as done in this study, is an aspect of primary importance and which cannot be disregarded in order to achieve reliable results.…”

Section: Discussionmentioning

confidence: 99%

Unraveling the Factors Determining Development of Type 2 Diabetes in Women With a History of Gestational Diabetes Mellitus Through Machine-Learning Techniques

et al. 2022

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Gestational diabetes mellitus (GDM) is a type of diabetes that usually resolves at the end of the pregnancy but exposes to a higher risk of developing type 2 diabetes mellitus (T2DM). This study aimed to unravel the factors, among those that quantify specific metabolic processes, which determine progression to T2DM by using machine-learning techniques. Classification of women who did progress to T2DM (labeled as PROG, n = 19) vs. those who did not (labeled as NON-PROG, n = 59) progress to T2DM has been performed by using Orange software through a data analysis procedure on a generated data set including anthropometric data and a total of 34 features, extracted through mathematical modeling/methods procedures. Feature selection has been performed through decision tree algorithm and then Naïve Bayes and penalized (L2) logistic regression were used to evaluate the ability of the selected features to solve the classification problem. Performance has been evaluated in terms of area under the operating receiver characteristics (AUC), classification accuracy (CA), precision, sensitivity, specificity, and F1. Feature selection provided six features, and based on them, classification was performed as follows: AUC of 0.795, 0.831, and 0.884; CA of 0.827, 0.813, and 0.840; precision of 0.830, 0.854, and 0.834; sensitivity of 0.827, 0.813, and 0.840; specificity of 0.700, 0.821, and 0.662; and F1 of 0.828, 0.824, and 0.836 for tree algorithm, Naïve Bayes, and penalized logistic regression, respectively. Fasting glucose, age, and body mass index together with features describing insulin action and secretion may predict the development of T2DM in women with a history of GDM.

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Section: Discussionmentioning

confidence: 99%

Unraveling the Factors Determining Development of Type 2 Diabetes in Women With a History of Gestational Diabetes Mellitus Through Machine-Learning Techniques

et al. 2022

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“…The regression problem was to predict how long a negative sample survived after the diagnosis. The regression performance was evaluated by the mean absolute error (MAE), as similar in [33,34]. MAE was defined as (…”

Section: B Performance Evaluation Metricsmentioning

confidence: 99%

Survival Time Prediction of Breast Cancer Patients Using Feature Selection Algorithm Crystall

Liu

Zheng

et al. 2021

IEEE Access

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Breast cancer is one of main causes of death for women. Most of the existing survival analyses focus on the features' associations with whether the patients may survive five years or not. The personalized question remains largely unresolved about how long a breast cancer patient will live. This study aims to predict the patient-specific survival time of breast cancer patients. It formulates the personalized question into two machine learning problems. The first problem is the binary classification of whether a patient will live longer than five years or not. The second one is to build a regression model to predict the patient's survival time within five years. The methylome of a breast cancer patient is used for the prediction. A new algorithm Crystall is presented to find the methylomic features for this regression model. Our models perform well in the above two problems, and achieve the mean absolute error (MAE) of about 1 month for predicting how long a breast cancer patient will live within five years. The detected biomarker genes demonstrate close connections with breast cancers.

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“…ISF estimated = ISF target − ratio ISF × abs(error glycemia ) = ISF target − ratio ISF × abs sgv current − sgv target (8) Equation ( 8)-Estimated Insulin Sensitivity Factor. This adjustment is optional and can be disabled, setting the ratio to zero.…”

Section: Proposed Algorithm To Dynamically Adjust the Insulin Sensitivity Factor (Isf)mentioning

confidence: 99%

“…Studies show that basal needs change depending on factors such as the time of the day, exercise [ 6 ], stress, illnesses [ 7 ], cholesterol or age [ 8 ]. Most commercial systems assume that basal needs for a specific patient are the same from day to day and, therefore, only one basal profile is used and refined using past days’ information.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Insulin Basal Needs Estimation and Parameters Adjustment in Type 1 Diabetes

Berián

Bravo

Gardel

et al. 2021

Sensors

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Technology advances have made possible improvements such as Continuous Glucose Monitors, giving the patient a glucose reading every few minutes, or insulin pumps, allowing more personalized therapies. With the increasing number of available closed-loop systems, new challenges appear regarding algorithms and functionalities. Several of the analysed systems in this paper try to adapt to changes in some patients’ conditions and, in several of these systems, other variables such as basal needs are considered fixed from day to day to simplify the control problem. Therefore, these systems require a correct adjustment of the basal needs profile which becomes crucial to obtain good results. In this paper a novel approach tries to dynamically determine the insulin basal needs of the patient and use this information within a closed-loop algorithm, allowing the system to dynamically adjust in situations of illness, exercise, high-fat-content meals or even partially blocked infusion sites and avoiding the need for setting a basal profile that approximately matches the basal needs of the patient. The insulin sensitivity factor and the glycemic target are also dynamically modified according to the situation of the patient. Basal insulin needs are dynamically determined through linear regression via the decomposition of previously dosed insulin and its effect on the patient’s glycemia. Using the obtained value as basal insulin needs and other mechanisms such as basal needs modification through its trend, ISF and glycemic targets modification and low-glucose-suspend threshold, the safety of the algorithm is improved. The dynamic basal insulin needs determination was successfully included in a closed-loop control algorithm and was simulated on 30 virtual patients (10 adults, 10 adolescent and 10 children) using an open-source python implementation of the FDA-approved (Food and Drug Administration) UVa (University of Virginia)/Padova Simulator. Simulations showed that the proposed system dynamically determines the basal needs and can adapt to a partial blockage of the insulin infusion, obtaining similar results in terms of time in range to the case in which no blockage was simulated. The proposed algorithm can be incorporated to other current closed-loop control algorithms to directly estimate the patient’s basal insulin needs or as a monitoring channel to detect situations in which basal needs may differ from the expected ones.

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TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records

Cited by 25 publications

References 33 publications

Unraveling the Factors Determining Development of Type 2 Diabetes in Women With a History of Gestational Diabetes Mellitus Through Machine-Learning Techniques

Unraveling the Factors Determining Development of Type 2 Diabetes in Women With a History of Gestational Diabetes Mellitus Through Machine-Learning Techniques

Survival Time Prediction of Breast Cancer Patients Using Feature Selection Algorithm Crystall

Dynamic Insulin Basal Needs Estimation and Parameters Adjustment in Type 1 Diabetes

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