Publications by authors named "S M Shahnawaz Bin Tabib"

Article Synopsis
  • The study explores the challenges of assessing osteoporosis in Iran, focusing on the high costs and limited availability of traditional diagnostic tools.
  • Various machine learning methods were used to evaluate osteoporosis risk factors, finding that the extreme gradient boosting (XGB) model offered the highest accuracy (0.79) and AUC (0.78) compared to other methods like random forest and decision trees.
  • Implementing ML algorithms like XGB for early osteoporosis identification could reduce health complications and financial burdens on healthcare systems, addressing a critical public health issue.
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We are addressing the problem of estimating conditional average treatment effects with a continuous treatment and a continuous response, using random forests. We explore two general approaches: building trees with a split rule that seeks to increase the heterogeneity of the treatment effect estimation and building trees to predict as a proxy target variable. We conduct a simulation study to investigate several aspects including the presence or absence of confounding and colliding effects and the merits of locally centering the treatment and/or the response.

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Organic-inorganic hybrid nanoflowers (HNFs) of laccase@Zn(PO) were fabricated through a facile, simple, and rapid one-step strategy. In this process, laccase was involved in nucleation and fast anisotropic growth reactions with Zn (II) and phosphate ions. The average pore size of the prepared HNFs was 54.

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Objectives: The diagnosis of hepatopulmonary syndrome (HPS) which is a common complication in cirrhotic patients is still subject to debate. This study investigated the association of clinical findings with HPS in cirrhotic patients using macro-aggregated albumin lung perfusion scan (Tc-MAA lung scintigraphy). In addition, comparison between Tc-MAA lung scintigraphy and contrast echocardiography (CEE) in detection of HPS was also performed.

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