291 research outputs found

    Monotone Tree-Based GAMI Models by Adapting XGBoost

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    Recent papers have used machine learning architecture to fit low-order functional ANOVA models with main effects and second-order interactions. These GAMI (GAM + Interaction) models are directly interpretable as the functional main effects and interactions can be easily plotted and visualized. Unfortunately, it is not easy to incorporate the monotonicity requirement into the existing GAMI models based on boosted trees, such as EBM (Lou et al. 2013) and GAMI-Lin-T (Hu et al. 2022). This paper considers models of the form f(x)=∑j,kfj,k(xj,xk)f(x)=\sum_{j,k}f_{j,k}(x_j, x_k) and develops monotone tree-based GAMI models, called monotone GAMI-Tree, by adapting the XGBoost algorithm. It is straightforward to fit a monotone model to f(x)f(x) using the options in XGBoost. However, the fitted model is still a black box. We take a different approach: i) use a filtering technique to determine the important interactions, ii) fit a monotone XGBoost algorithm with the selected interactions, and finally iii) parse and purify the results to get a monotone GAMI model. Simulated datasets are used to demonstrate the behaviors of mono-GAMI-Tree and EBM, both of which use piecewise constant fits. Note that the monotonicity requirement is for the full model. Under certain situations, the main effects will also be monotone. But, as seen in the examples, the interactions will not be monotone.Comment: 12 page

    The Hamiltonian BRST quantization of a noncommutative nonabelian gauge theory and its Seiberg-Witten map

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    We consider the Hamiltonian BRST quantization of a noncommutative non abelian gauge theory. The Seiberg-Witten map of all phase-space variables, including multipliers, ghosts and their momenta, is given in first order in the noncommutative parameter Ξ\theta. We show that there exists a complete consistence between the gauge structures of the original and of the mapped theories, derived in a canonical way, once we appropriately choose the map solutions.Comment: 10 pages, Latex. Address adde

    Gender-based difference in early mortality among patients with ST-segment elevation myocardial infarction: insights from Kermanshah STEMI Registry.

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    Introduction: This study aimed to evaluate the in-hospital mortality of patients with ST-segment elevation myocardial infarction (STEMI), according to gender and other likely risk factors. Methods: This study reports on data relating to 1,484 consecutive patients with STEMI registered from June 2016 to May 2018 in the Western Iran STEMI Registry. Data were collected using a standardized case report developed by the European Observational Registry Program (EORP). The relationship between in-hospital mortality and potential predicting variables was assessed multivariable logistic regression. Differences between groups in mortality rates were compared using chi-square tests and independent t-tests. Results: Out of the 1484 patients, 311(21%) were female. Women were different from men in terms of age (65.8 vs. 59), prevalence of hypertension (HTN) (63.7% vs. 35.4%), diabetes mellitus (DM) (37.7% vs. 16.2%), hypercholesterolemia (36.7% vs. 18.5%) and the history of previous congestive heart failure (CHF) (6.6% vs. 3.0%). Smoking was more prevalent among men (55.9% vs. 13.2%). Although the in-hospital mortality rate was higher in women (11.6% vs. 5.5%), after adjusting for other risk factors, female sex was not an independent predictor for in-hospital mortality. Multivariable analysis identified that age and higher Killip class (≄II) were significantly associated with in-hospital mortality rate. Conclusion: In-hospital mortality after STEMI in women was higher than men. However, the role of sex as an independent predictor of mortality disappeared in regression analysis. The gender based difference in in-hospital mortality after STEMI may be related to the poorer cardiovascular disease (CVD) risk factor profile of the women

    BRST Quantization of Noncommutative Gauge Theories

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    In this paper, the BRST symmetry transformation is presented for the noncommutative U(N) gauge theory. The nilpotency of the charge associated to this symmetry is then proved. As a consequence for the space-like non-commutativity parameter, the Hilbert space of physical states is determined by the cohomology space of the BRST operator as in the commutative case. Further, the unitarity of the S-matrix elements projected onto the subspace of physical states is deduced.Comment: 20 pages, LaTeX, no figures, one reference added, to appear in Phys. Rev.

    Comparison of Magnetic Resonance Imaging-Based Risk Calculators to Predict Prostate Cancer Risk

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    Importance: Magnetic resonance imaging (MRI)-based risk calculators can replace or augment traditional prostate cancer (PCa) risk prediction tools. However, few data are available comparing performance of different MRI-based risk calculators in external cohorts across different countries or screening paradigms. Objective: To externally validate and compare MRI-based PCa risk calculators (Prospective Loyola University Multiparametric MRI [PLUM], UCLA [University of California, Los Angeles]-Cornell, Van Leeuwen, and Rotterdam Prostate Cancer Risk Calculator-MRI [RPCRC-MRI]) in cohorts from Europe and North America. Design, Setting, and Participants: This multi-institutional, external validation diagnostic study of 3 unique cohorts was performed from January 1, 2015, to December 31, 2022. Two cohorts from Europe and North America used MRI before biopsy, while a third cohort used an advanced serum biomarker, the Prostate Health Index (PHI), before MRI or biopsy. Participants included adult men without a PCa diagnosis receiving MRI before prostate biopsy. Interventions: Prostate MRI followed by prostate biopsy. Main Outcomes and Measures: The primary outcome was diagnosis of clinically significant PCa (grade group ≄2). Receiver operating characteristics for area under the curve (AUC) estimates, calibration plots, and decision curve analysis were evaluated. Results: A total of 2181 patients across the 3 cohorts were included, with a median age of 65 (IQR, 58-70) years and a median prostate-specific antigen level of 5.92 (IQR, 4.32-8.94) ng/mL. All models had good diagnostic discrimination in the European cohort, with AUCs of 0.90 for the PLUM (95% CI, 0.86-0.93), UCLA-Cornell (95% CI, 0.86-0.93), Van Leeuwen (95% CI, 0.87-0.93), and RPCRC-MRI (95% CI, 0.86-0.93) models. All models had good discrimination in the North American cohort, with an AUC of 0.85 (95% CI, 0.80-0.89) for PLUM and AUCs of 0.83 for the UCLA-Cornell (95% CI, 0.80-0.88), Van Leeuwen (95% CI, 0.79-0.88), and RPCRC-MRI (95% CI, 0.78-0.87) models, with somewhat better calibration for the RPCRC-MRI and PLUM models. In the PHI cohort, all models were prone to underestimate clinically significant PCa risk, with best calibration and discrimination for the UCLA-Cornell (AUC, 0.83 [95% CI, 0.81-0.85]) model, followed by the PLUM model (AUC, 0.82 [95% CI, 0.80-0.84]). The Van Leeuwen model was poorly calibrated in all 3 cohorts. On decision curve analysis, all models provided similar net benefit in the European cohort, with higher benefit for the PLUM and RPCRC-MRI models at a threshold greater than 22% in the North American cohort. The UCLA-Cornell model demonstrated highest net benefit in the PHI cohort. Conclusions and Relevance: In this external validation study of patients receiving MRI and prostate biopsy, the results support the use of the PLUM or RPCRC-MRI models in MRI-based screening pathways regardless of European or North American setting. However, tools specific to screening pathways incorporating advanced biomarkers as reflex tests are needed due to underprediction.</p
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