Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme—A Post Hoc Analysis

Huang, Yung-Chuan; Cheng, Yu‐Chen; Jhou, Mao-Jhen; Chen, Mingchih; Lu, Chi-Jie

doi:10.3390/jpm12050756

Cited by 16 publications

(15 citation statements)

References 64 publications

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“…Given the fact that the performance of prediction depends on the data source and methods used, we hypothesized that ML methods applied to a large and recent patient dataset with a wide range of variables could produce a risk model with superior performance compared to these existing scores. Indeed, ML methods have already been used in a few studies to predict vascular events, such as bleeding events [ 33 , 34 ]. However, once again, these studies were focused on specific contexts and did not consider the prescribing determinants (DDI, PIM, and PIM–DDI) in their analysis.…”

Section: Discussionmentioning

confidence: 99%

Drug–Drug Interactions with Oral Anticoagulants as Potentially Inappropriate Medications: Prevalence and Outcomes in Elderly Patients in Primary Care and Hospital Settings

et al. 2022

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Direct oral anticoagulants and vitamin K antagonists are considered as potentially inappropriate medications (PIM) in several situations according to Beers Criteria. Drug–drug interactions (DDI) occurring specifically with these oral anticoagulants considered PIM (PIM–DDI) is an issue since it could enhance their inappropriate character and lead to adverse drug events, such as bleeding events. The aim of this study was (1) to describe the prevalence of oral anticoagulants as PIM, DDI and PIM–DDI in elderly patients in primary care and during hospitalization and (2) to evaluate their potential impact on the clinical outcomes by predicting hospitalization for bleeding events using machine learning methods. This retrospective study based on the linkage between a primary care database and a hospital data warehouse allowed us to display the oral anticoagulant treatment pathway. The prevalence of PIM was similar between primary care and hospital setting (22.9% and 20.9%), whereas the prevalence of DDI and PIM–DDI were slightly higher during hospitalization (47.2% vs. 58.9% and 19.5% vs. 23.5%). Concerning mechanisms, combined with CYP3A4–P-gp interactions as PIM–DDI, were among the most prevalent in patients with bleeding events. Although PIM, DDI and PIM–DDI did not appeared as major predictors of bleeding events, they should be considered since they are the only factors that can be optimized by pharmacist and clinicians.

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Section: Discussionmentioning

confidence: 99%

Drug–Drug Interactions with Oral Anticoagulants as Potentially Inappropriate Medications: Prevalence and Outcomes in Elderly Patients in Primary Care and Hospital Settings

et al. 2022

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“…This algorithm can sort objects according to specific characteristics and variables based on the Bayes theorem, estimating the values of dependent variable ( y ). 32…”

Section: Methodsmentioning

confidence: 99%

“…This algorithm can sort objects according to specific characteristics and variables based on the Bayes theorem, estimating the values of dependent variable (y). 32 The third method in this study is RF, an ensemble learning decision tree algorithm that combines bootstrap resampling and bagging. 33 RF works by randomly generating many different and unpruned CART decision trees, where the decrease in Gini impurity is used as the splitting criterion.…”

Section: Proposed Schemementioning

confidence: 99%

Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort

Tzou,

Peng,

Huang

et al. 2023

Journal of the Chinese Medical Association

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Background: Population aging is emerging as an increasingly acute challenge for countries around the world. One particular manifestation of this phenomenon is the impact of osteoporosis on individuals and national health systems. Previous studies of risk factors for osteoporosis were conducted using traditional statistical methods, but more recent efforts have turned to machine learning approaches. Most such efforts, however, treat the target variable (bone mineral density or fracture rate) as a categorical one which provides no quantitative information. The present study uses five different machine learning methods to analyze the risk factors for T-score of bone mineral density, seeking to: (1) Compare the prediction accuracy between different machine learning methods and traditional multiple linear regression and (2) rank the importance of 25 different risk factors. Methods: The study sample includes 24,412 women aged > 55 y/o with 25 related variables, applying traditional multiple linear regression (MLR) and five different machine learning methods: classification and regression tree (CART), Naïve Bayes (NB), Random Forest (RF), stochastic gradient boosting (SGB), and eXtreme Gradient Boosting (XGBoost). The metrics used for model performance comparisons are the symmetric mean absolute percentage error, and relative absolute error, root relative squared error and root mean squared error. Results: Machine learning approaches outperformed MLR for all four prediction errors. The average importance ranking of each factor generated by the machine learning methods indicate that age is the most important factor determining T-score, followed by eGFR, BMI, UA, and education level. Conclusion: In a group of women aged > 55 years, we demonstrated that machine learning methods provide superior performance in estimating T-Score, with age being the most important impact factor, followed by eGFR, BMI, UA, and education level.

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“…The application of data-driven machine learning (ML) algorithms to the analysis of healthcare data and/or medical records is not uncommon, and there is even an increasing trend in publications introducing artificial intelligence technology [ 20 , 21 , 22 , 23 ]. The advantages of ML algorithms include the effective investigation of complex relationships between risk factors and outcomes, and promising predictive performance with vast amounts of medical data [ 22 , 23 , 24 , 25 , 26 ]. Our study used five ML techniques—stochastic gradient boosting (SGB), multivariate adaptive regression splines (MARS), least absolute shrinkage and selection operator logistic regression (Lasso), ridge logistic regression (Ridge), and gradient boosting with categorical features support (CatBoost)—to develop a multi-stage ML algorithm-based prediction scheme.…”

Section: Introductionmentioning

confidence: 99%

Integrating Health Data-Driven Machine Learning Algorithms to Evaluate Risk Factors of Early Stage Hypertension at Different Levels of HDL and LDL Cholesterol

et al. 2022

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Purpose: Cardiovascular disease (CVD) is a major worldwide health burden. As the risk factors of CVD, hypertension, and hyperlipidemia are most mentioned. Early stage hypertension in the population with dyslipidemia is an important public health hazard. This study was the application of data-driven machine learning (ML), demonstrating complex relationships between risk factors and outcomes and promising predictive performance with vast amounts of medical data, aimed to investigate the association between dyslipidemia and the incidence of early stage hypertension in a large cohort with normal blood pressure at baseline. Methods: This study analyzed annual health screening data for 71,108 people from 2005 to 2017, including data for 27 risk-related indicators, sourced from the MJ Group, a major health screening center in Taiwan. We used five machine learning (ML) methods—stochastic gradient boosting (SGB), multivariate adaptive regression splines (MARS), least absolute shrinkage and selection operator regression (Lasso), ridge regression (Ridge), and gradient boosting with categorical features support (CatBoost)—to develop a multi-stage ML algorithm-based prediction scheme and then evaluate important risk factors at the early stage of hypertension, especially for groups with high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) levels within or out of the reference range. Results: Age, body mass index, waist circumference, waist-to-hip ratio, fasting plasma glucose, and C-reactive protein (CRP) were associated with hypertension. The hemoglobin level was also a positive contributor to blood pressure elevation and it appeared among the top three important risk factors in all LDL-C/HDL-C groups; therefore, these variables may be important in affecting blood pressure in the early stage of hypertension. A residual contribution to blood pressure elevation was found in groups with increased LDL-C. This suggests that LDL-C levels are associated with CPR levels, and that the LDL-C level may be an important factor for predicting the development of hypertension. Conclusion: The five prediction models provided similar classifications of risk factors. The results of this study show that an increase in LDL-C is more important than the start of a drop in HDL-C in health screening of sub-healthy adults. The findings of this study should be of value to health awareness raising about hypertension and further discussion and follow-up research.

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Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme—A Post Hoc Analysis

Cited by 16 publications

References 64 publications

Drug–Drug Interactions with Oral Anticoagulants as Potentially Inappropriate Medications: Prevalence and Outcomes in Elderly Patients in Primary Care and Hospital Settings

Drug–Drug Interactions with Oral Anticoagulants as Potentially Inappropriate Medications: Prevalence and Outcomes in Elderly Patients in Primary Care and Hospital Settings

Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort

Integrating Health Data-Driven Machine Learning Algorithms to Evaluate Risk Factors of Early Stage Hypertension at Different Levels of HDL and LDL Cholesterol

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