Dataset Information

An Algorithm for Generating Individualized Treatment Decision Trees and Random Forests.

ABSTRACT: With new treatments and novel technology available, precision medicine has become a key topic in the new era of healthcare. Traditional statistical methods for precision medicine focus on subgroup discovery through identifying interactions between a few markers and treatment regimes. However, given the large scale and high dimensionality of modern data sets, it is difficult to detect the interactions between treatment and high dimensional covariates. Recently, novel approaches have emerged that seek to directly estimate individualized treatment rules (ITR) via maximizing the expected clinical reward by using, for example, support vector machines (SVM) or decision trees. The latter enjoys great popularity in clinical practice due to its interpretability. In this paper, we propose a new reward function and a novel decision tree algorithm to directly maximize rewards. We further improve a single tree decision rule by an ensemble decision tree algorithm, ITR random forests. Our final decision rule is an average over single decision trees and it is a soft probability rather than a hard choice. Depending on how strong the treatment recommendation is, physicians can make decisions based on our model along with their own judgment and experience. Performance of ITR forest and tree methods is assessed through simulations along with applications to a randomized controlled trial (RCT) of 1385 patients with diabetes and an EMR cohort of 5177 patients with diabetes. ITR forest and tree methods are implemented using statistical software R (https://github.com/kdoub5ha/ITR.Forest).

SUBMITTER: Doubleday K

PROVIDER: S-EPMC7286561 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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An Algorithm for Generating Individualized Treatment Decision Trees and Random Forests.

Doubleday Kevin K Zhou Hua H Fu Haoda H Zhou Jin J

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 20180614 4

With new treatments and novel technology available, precision medicine has become a key topic in the new era of healthcare. Traditional statistical methods for precision medicine focus on subgroup discovery through identifying interactions between a few markers and treatment regimes. However, given the large scale and high dimensionality of modern data sets, it is difficult to detect the interactions between treatment and high dimensional covariates. Recently, novel approaches have emerged that ...[more]

PMID: 32523325

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Project description:BackgroundPrognosis, recurrence rate, and secondary prevention strategies differ by different etiologies in acute ischemic stroke. However, identifying its cause is challenging.ObjectiveThis study aimed to develop a model to identify the cause of stroke using machine learning (ML) methods and test its accuracy.MethodsWe retrospectively reviewed the data of patients who had determined etiology defined by the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) from CASE-II (NCT04487340) to train and evaluate six ML models, namely, Random Forests (RF), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), Ada Boosting, Gradient Boosting Machine (GBM), for the detection of cardioembolism (CE), large-artery atherosclerosis (LAA), and small-artery occlusion (SAO). Between October 2016 and April 2020, patients were enrolled consecutively for algorithm development (phase one). Between June 2020 and December 2020, patients were enrolled consecutively in a test set for algorithm test (phase two). Area under the curve (AUC), precision, recall, accuracy, and F1 score were calculated for the prediction model.ResultsFinally, a total of 18,209 patients were enrolled in phase one, including 13,590 patients (i.e., 6,089 CE, 4,539 LAA, and 2,962 SAO) in the model, and a total of 3,688 patients were enrolled in phase two, including 3,070 patients (i.e., 1,103 CE, 1,269 LAA, and 698 SAO) in the model. Among the six models, the best models were RF, XGBoost, and GBM, and we chose the RF model as our final model. Based on the test set, the AUC values of the RF model to predict CE, LAA, and SAO were 0.981 (95%CI, 0.978-0.986), 0.919 (95%CI, 0.911-0.928), and 0.918 (95%CI, 0.908-0.927), respectively. The most important items to identify CE, LAA, and SAO were atrial fibrillation and degree of stenosis of intracranial arteries.ConclusionThe proposed RF model could be a useful diagnostic tool to help neurologists categorize etiologies of stroke.Clinical trial registration[www.ClinicalTrials.gov], identifier [NCT01274117].

Dataset Information

An Algorithm for Generating Individualized Treatment Decision Trees and Random Forests.

Publications

An Algorithm for Generating Individualized Treatment Decision Trees and Random Forests.

Similar Datasets

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