An interpretable boosting model to predict side effects of analgesics for osteoarthritis

Liu, Liangliang; Yu, Ying; Fei, Zhihui; Li, Min; Wu, Fang‐Xiang; Li, Hongdong; Pan, Yi; Wang, Jianxin

doi:10.1186/s12918-018-0624-4

Cited by 52 publications

(48 citation statements)

References 33 publications

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“…22,[36][37][38] In addition, although overfitting is a common limitation in refined non-linear machine learning algorithms, XGBoost supervises machine learning problems by parallel computing, regularization, cross validation, flexibility, or availability. 16,39,40 Comparison with previous research DeVries and associates previously reported that no clinically significant differences were observed between the use of unsupervised machine learning with complete admission neurological information and established standards. 10 They showed the inherent weakness of applying AUC to imbalanced data sets and outlined a new strategy to evaluate performance.…”

Section: Figmentioning

confidence: 75%

“…[10][11][12][13][14] Among different machine learning systems, extreme gradient boosting (XGBoost) is widely used to accomplish state-of-the-art analyses in diverse fields with good accuracy or area under the receiver operating characteristic curve (AUC). 15,16 XGBoost, a decision-tree-based ensemble machine learning algorithm with a gradient boosting framework, was developed by Chen and Guestrin. 17 It has since been used in traffic census and the field of energy consumption.…”

Section: Introductionmentioning

confidence: 99%

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XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury

Inoue

Ichikawa

Ueno

et al. 2020

Neurotrauma Reports

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The accurate prediction of neurological outcomes in patients with cervical spinal cord injury (SCI) is difficult because of heterogeneity in patient characteristics, treatment strategies, and radiographic findings. Although machine learning algorithms may increase the accuracy of outcome predictions in various fields, limited information is available on their efficacy in the management of SCI. We analyzed data from 165 patients with cervical SCI, and extracted important factors for predicting prognoses. Extreme gradient boosting (XGBoost) as a machine learning model was applied to assess the reliability of a machine learning algorithm to predict neurological outcomes compared with that of conventional methodology, such as a logistic regression or decision tree. We used regularly obtainable data as predictors, such as demographics, magnetic resonance variables, and treatment strategies. Predictive tools, including XGBoost, a logistic regression, and a decision tree, were applied to predict neurological improvements in the functional motor status (ASIA [American Spinal Injury Association] Impairment Scale [AIS] D and E) 6 months after injury. We evaluated predictive performance, including accuracy and the area under the receiver operating characteristic curve (AUC). Regarding predictions of neurological improvements in patients with cervical SCI, XGBoost had the highest accuracy (81.1%), followed by the logistic regression (80.6%) and the decision tree (78.8%). Regarding AUC, the logistic regression showed 0.877, followed by XGBoost (0.867) and the decision tree (0.753). XGBoost reliably predicted neurological alterations in patients with cervical SCI. The utilization of predictive machine learning algorithms may enhance personalized management choices through pre-treatment categorization of patients.

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Section: Figmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury

Inoue

Ichikawa

Ueno

et al. 2020

Neurotrauma Reports

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“…When adding new models, a gradient descent algorithm was used to minimize the loss. The XGBoost model was widely used for diagnosis classi cation [34,35], treatment effect [36,37], and prognosis evaluation [38,39] in different diseases.…”

Section: Discussionmentioning

confidence: 99%

Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma Using Machine-Learning Approaches

et al. 2020

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Asthma is a serious immune-mediated respiratory airway disease. Its pathological processes involve genetics and the environment, but it remains unclear. To understand the risk factors of asthma, we combined genome-wide association study (GWAS) risk loci and clinical data in predicting asthma using machine-learning approaches. A case-control study with 123 asthma patients and 100 healthy controls was conducted in Zhuang population in Guangxi. GWAS risk loci were detected using polymerase chain reaction, and clinical data were collected. Machine-learning approaches (e.g., extreme gradient boosting [XGBoost], decision tree, support vector machine, and random forest algorithms) were used to identify the major factors that contributed to asthma. A total of 14 GWAS risk loci with clinical data were analyzed on the basis of 10 times of 10-fold cross-validation for all machine-learning models. Using GWAS risk loci or clinical data, the best performances were area under the curve (AUC) values of 64.3% and 71.4%, respectively. Combining GWAS risk loci and clinical data, the XGBoost established the best model with an AUC of 79.7%, indicating that the combination of genetics and clinical data can enable improved performance. We then sorted the importance of features and found that the top six risk factors for predicting asthma were rs3117098, rs7775228, family history, rs2305480, rs4833095, and body mass index. Asthma-prediction models based on GWAS risk loci and clinical data can accurately predict asthma and thus provide insights into the disease pathogenesis of asthma. Further research is required to evaluate more genetic markers and clinical data and predict asthma risk.

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“…Using the R package Caret ( CRAN: Caret ) 59 , we developed three (3) models with inherent five (5)-fold cross validation (CV), such that we obtained test set accuracy on running each model. These models included Random Forest 60 , Extreme Gradient Boosting 61 , and Gradient Boosting Machine (GBM) 62 . Testing these models with five-pronged, FDA-adherent teratogenicity scores, we found that GBM yielded the highest predictive accuracy.…”

Section: Methodsmentioning

confidence: 99%

Machine learning on drug-specific data to predict small molecule teratogenicity

Challa

Beam

Shen

et al. 2019

Preprint

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1Pregnant women are an especially vulnerable population, given the sensitivity of a developing 2 fetus to chemical exposures. However, prescribing behavior for the gravid patient is guided on 3 limited human data and conflicting cases of adverse outcomes due to the exclusion of pregnant 4 populations from randomized, controlled trials. These factors increase risk for adverse drug 5 outcomes and reduce quality of care for pregnant populations. Herein, we propose the 6 application of artificial intelligence to systematically predict the teratogenicity of a prescriptible 7 small molecule from information inherent to the drug. Using unsupervised and supervised 8 machine learning, our model probes all small molecules with known structure and teratogenicity 9 data published in research-amenable formats to identify patterns among structural, meta-10 structural, and in vitro bioactivity data for each drug and its teratogenicity score. With this 11 workflow, we discovered three chemical functionalities that predispose a drug towards increased 12 teratogenicity and two moieties with potentially protective effects. Our models predict three 13 clinically-relevant classes of teratogenicity with AUC = 0.8 and nearly double the predictive 14 accuracy of a blind control for the same task, suggesting successful modeling. We also present 15 extensive barriers to translational research that restrict data-driven studies in pregnancy and 16 therapeutically "orphan" pregnant populations. Collectively, this work represents a first-in-kind 17 platform for the application of computing to study and predict teratogenicity. 18

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An interpretable boosting model to predict side effects of analgesics for osteoarthritis

Cited by 52 publications

References 33 publications

XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury

XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury

Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma Using Machine-Learning Approaches

Machine learning on drug-specific data to predict small molecule teratogenicity

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