Comparison of multivariate linear regression and a machine learning algorithm developed for prediction of precision warfarin dosing in a Korean population

Nguyen, Van Lam; Nguyen, Hoang Dat; Cho, Yong‐Soon; Kim, Ho-Sook; Han, Il-Yong; Kim, Dae-Kyeong; Ahn, Sang Ho; Shin, Jae‐Gook

doi:10.1111/jth.15318

Cited by 24 publications

(9 citation statements)

References 42 publications

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“…A number of machine learning studies have leveraged International the Warfarin Pharmacogenetics Consortium dataset of 5700 patients from 21 countries, however many of these have incorporated genetic markers associated with warfarin metabolism 46 – 50 , which may offer valuable performance improvements, but cannot be easily integrated into routine clinical practice (while single nucleotide polymorphisms CYP2C9 and VKORC1 were available for a subset of our patients, we elected not to use them in order to enhance the generalizability of our model for real world clinical settings, especially low and middle income countries where warfarin is still routinely used). Ours is also the first study we are aware of to evaluate the use of machine learning for the long-term dynamic management of warfarin in the community, versus warfarin initiation or short-term management during a hospital stay 42 – 47 . Our study is the largest its kind by a considerable margin, which is relevant given the data-hungry nature of machine learning methods.…”

Section: Discussionmentioning

confidence: 99%

Optimizing warfarin dosing for patients with atrial fibrillation using machine learning

Petch,

Nelson,

et al. 2024

Sci Rep

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While novel oral anticoagulants are increasingly used to reduce risk of stroke in patients with atrial fibrillation, vitamin K antagonists such as warfarin continue to be used extensively for stroke prevention across the world. While effective in reducing the risk of strokes, the complex pharmacodynamics of warfarin make it difficult to use clinically, with many patients experiencing under- and/or over- anticoagulation. In this study we employed a novel implementation of deep reinforcement learning to provide clinical decision support to optimize time in therapeutic International Normalized Ratio (INR) range. We used a novel semi-Markov decision process formulation of the Batch-Constrained deep Q-learning algorithm to develop a reinforcement learning model to dynamically recommend optimal warfarin dosing to achieve INR of 2.0–3.0 for patients with atrial fibrillation. The model was developed using data from 22,502 patients in the warfarin treated groups of the pivotal randomized clinical trials of edoxaban (ENGAGE AF-TIMI 48), apixaban (ARISTOTLE) and rivaroxaban (ROCKET AF). The model was externally validated on data from 5730 warfarin-treated patients in a fourth trial of dabigatran (RE-LY) using multilevel regression models to estimate the relationship between center-level algorithm consistent dosing, time in therapeutic INR range (TTR), and a composite clinical outcome of stroke, systemic embolism or major hemorrhage. External validation showed a positive association between center-level algorithm-consistent dosing and TTR (R2 = 0.56). Each 10% increase in algorithm-consistent dosing at the center level independently predicted a 6.78% improvement in TTR (95% CI 6.29, 7.28; p < 0.001) and a 11% decrease in the composite clinical outcome (HR 0.89; 95% CI 0.81, 1.00; p = 0.015). These results were comparable to those of a rules-based clinical algorithm used for benchmarking, for which each 10% increase in algorithm-consistent dosing independently predicted a 6.10% increase in TTR (95% CI 5.67, 6.54, p < 0.001) and a 10% decrease in the composite outcome (HR 0.90; 95% CI 0.83, 0.98, p = 0.018). Our findings suggest that a deep reinforcement learning algorithm can optimize time in therapeutic range for patients taking warfarin. A digital clinical decision support system to promote algorithm-consistent warfarin dosing could optimize time in therapeutic range and improve clinical outcomes in atrial fibrillation globally.

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Section: Discussionmentioning

confidence: 99%

Optimizing warfarin dosing for patients with atrial fibrillation using machine learning

Petch,

Nelson,

et al. 2024

Sci Rep

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“…The average audio features of the frames were used as input features of the fatigue level classifiers. By using P300 as ground truth, we trained several commonly used classifiers, including linear regression (LR) ( Nguyen et al, 2021 ), linear discriminant analysis (LDA) ( Dornaika and Khoder, 2020 ), K-nearest neighbor (KNN) ( Abu Alfeilat et al, 2019 ), classification and regression trees (CART) ( Johns et al, 2021 ), naive Bayes classifier (NB) ( Sugahara and Ueno, 2021 ), support vector machine (SVM) ( Huang et al, 2018 ), and multilayer perceptron (MLP) ( Panghal and Kumar, 2021 ), to classify the fatigue level of each audio input. Leave-one-out (LOO) cross-validation ( Luo et al, 2015 ) was used to guarantee the generalization performance of our models.…”

Section: Methodsmentioning

confidence: 99%

A rapid, non-invasive method for fatigue detection based on voice information

Gao¹,

Ma²,

Yang³

et al. 2022

Front. Cell Dev. Biol.

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Fatigue results from a series of physiological and psychological changes due to continuous energy consumption. It can affect the physiological states of operators, thereby reducing their labor capacity. Fatigue can also reduce efficiency and, in serious cases, cause severe accidents. In addition, it can trigger pathological-related changes. By establishing appropriate methods to closely monitor the fatigue status of personnel and relieve the fatigue on time, operation-related injuries can be reduced. Existing fatigue detection methods mostly include subjective methods, such as fatigue scales, or those involving the use of professional instruments, which are more demanding for operators and cannot detect fatigue levels in real time. Speech contains information that can be used as acoustic biomarkers to monitor physiological and psychological statuses. In this study, we constructed a fatigue model based on the method of sleep deprivation by collecting various physiological indexes, such as P300 and glucocorticoid level in saliva, as well as fatigue questionnaires filled by 15 participants under different fatigue procedures and graded the fatigue levels accordingly. We then extracted the speech features at different instances and constructed a model to match the speech features and the degree of fatigue using a machine learning algorithm. Thus, we established a method to rapidly judge the degree of fatigue based on speech. The accuracy of the judgment based on unitary voice could reach 94%, whereas that based on long speech could reach 81%. Our fatigue detection method based on acoustic information can easily and rapidly determine the fatigue levels of the participants. This method can operate in real time and is non-invasive and efficient. Moreover, it can be combined with the advantages of information technology and big data to expand its applicability.

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“…Although the accuracy of the linear regression model is also affected to a certain extent (Overfitting, as mentioned above), the performance of linear regression is still slightly higher than the other three algorithms when the sample is small. Related research also shows that the linear regression algorithm and its improved algorithm show prediction accuracy comparable to random forest, support vector machine, and the random generalized linear model (Cardoso-Silva et al, 2019;Rath et al, 2020;Meulenbroek and Pichardo, 2021;Nguyen et al, 2021).…”

Section: Prediction Effects Of the Four Algorithm Modelsmentioning

confidence: 97%

Automatic Decision-Making Style Recognition Method Using Kinect Technology

et al. 2022

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In recent years, somatosensory interaction technology, represented by Microsoft’s Kinect hardware platform, has been widely used in various fields, such as entertainment, education, and medicine. Kinect technology can easily capture and record behavioral data, which provides new opportunities for behavioral and psychological correlation analysis research. In this paper, an automatic decision-style recognition method is proposed. Experiments involving 240 subjects were conducted to obtain face data and individual decision-making style score. The face data was obtained using the Kinect camera, and the decision-style score were obtained via a questionnaire. To realize automatic recognition of an individual decision-making style, machine learning was employed to establish the mapping relationship between the face data and a scaled evaluation of the decision-making style score. This study adopts a variety of classical machine learning algorithms, including Linear regression, Support vector machine regression, Ridge regression, and Bayesian ridge regression. The experimental results show that the linear regression model returns the best results. The correlation coefficient between the linear regression model evaluation results and the scale evaluation results was 0.6, which represents a medium and higher correlation. The results verify the feasibility of automatic decision-making style recognition method based on facial analysis.

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Comparison of multivariate linear regression and a machine learning algorithm developed for prediction of precision warfarin dosing in a Korean population

Cited by 24 publications

References 42 publications

Optimizing warfarin dosing for patients with atrial fibrillation using machine learning

Optimizing warfarin dosing for patients with atrial fibrillation using machine learning

A rapid, non-invasive method for fatigue detection based on voice information

Automatic Decision-Making Style Recognition Method Using Kinect Technology

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