Procalcitonin-guided antibiotic usage - addressing heterogeneity in meta-analysis

Birajdar, Amit Ravindra; Thatte, Urmila M; Gogtay, Nithya J

doi:10.4103/ijmr.ijmr_1235_18

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…75 Therefore, after a large complex set of potential predictors was gathered, the next step was to remove redundant, unimportant, and strongly correlated predictors to avoid unreliable and unstable estimates from the regression models. Therefore, LASSO regularization, 76,77 F-test, 78 correlation/collinearity analysis, 79,80 and prior domain knowledge were used to remove redundant and strongly correlated predictors and select the most important predictors of the response variable (density/ viscosity).…”

Section: ■ Methods and Materialsmentioning

confidence: 99%

Temperature-Dependent Density and Viscosity Prediction for Hydrocarbons: Machine Learning and Molecular Dynamics Simulations

Panwar,

Yang,

Martini

2023

J. Chem. Inf. Model.

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Machine learning-based predictive models allow rapid and reliable prediction of material properties and facilitate innovative materials design. Base oils used in the formulation of lubricant products are complex hydrocarbons of varying sizes and structure. This study developed Gaussian process regression-based models to accurately predict the temperature-dependent density and dynamic viscosity of 305 complex hydrocarbons. In our approach, strongly correlated/collinear predictors were trimmed, important predictors were selected by least absolute shrinkage and selection operator (LASSO) regularization and prior domain knowledge, hyperparameters were systematically optimized by Bayesian optimization, and the models were interpreted. The approach provided versatile and quantitative structure–property relationship (QSPR) models with relatively simple predictors for determining the dynamic viscosity and density of complex hydrocarbons at any temperature. In addition, we developed molecular dynamics simulation-based descriptors and evaluated the feasibility and versatility of dynamic descriptors from simulations for predicting the material properties. It was found that the models developed using a comparably smaller pool of dynamic descriptors performed similarly in predicting density and viscosity to models based on many more static descriptors. The best models were shown to predict density and dynamic viscosity with coefficient of determination (R 2) values of 99.6% and 97.7%, respectively, for all data sets, including a test data set of 45 molecules. Finally, partial dependency plots (PDPs), individual conditional expectation (ICE) plots, local interpretable model-agnostic explanation (LIME) values, and trimmed model R 2 values were used to identify the most important static and dynamic predictors of the density and viscosity.

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