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This article provides a curated review of selected papers published in prominent economics journals that use machine learning (ML) tools for research and policy analysis. The review focuses on three key questions: (1) when ML is used in economics, (2) what ML models are commonly preferred, and (3) how they are used for economic applications. The review highlights that ML is particularly used to process nontraditional and unstructured data, capture strong nonlinearity, and improve prediction accuracy. Deep learning models are suitable for nontraditional data, whereas ensemble learning models are preferred for traditional datasets. While traditional econometric models may suffice for analyzing low-complexity data, the increasing complexity of economic data due to rapid digitalization and the growing literature suggests that ML is becoming an essential addition to the econometrician's toolbox.

We performed a retrospective analysis of 146 chronic total occlusion CTO patients to evaluate the antecedents of success and failure in CTO – Percutaneous Coronary Intervention (PCI) in Indian patients. The study aimed to identify the technical success rate, analyse immediate patient outcomes, and understand the factors impacting the successful outcomes. Our results showed that J-CTO (Multicenter CTO Registry of Japan) scores correlate well with the success rates of CTO-PCI and two most important factors deciding failure are lesion length more than 20 mm and lesions with calcification. Most important step to success of CTO is wiring, once wire crosses the segment, success rates of the procedure is around 97%. The wire escalation strategy has to be modified once the initial soft (polymer) wire fails, it’s reasonable to use high tip load wire like conquest pro without the use of intermediate wires (except in presence of tortuosity). At 1 year follow up of these patients, there was a statistically significant drop in angina class and major adverse cardiac event rates in the successful CTO group. Keywords: CTO Success predictors

The spread of COVID-19 has caused large-scale loss of life and economic damage. This pandemic has had a swift effect on the macroeconomy, posing a new and different shock to the Canadian economy. Governments have responded in many ways, including through public health measures, fiscal stimulus and monetary policy. Policy-makers seek to understand the current state of the economy for their COVID-19 support to be effective. However, major economic indicators are released with a substantial delay. This problem is usually dealt with by using a linear model of past economic variables. But this is not the best approach presently, given the large and nonlinear effects of COVID-19 on the economy. In this paper, we develop a model to predict the current state of the economy—known as nowcasting—using retail payments system data and machine learning. The Canadian retail payments data aid in understanding the current state of the economy because they include many types of transactions and are available daily. These data features are ideal for macroeconomic nowcasting during a crisis. The flexibility of machine learning can help capture the large and nonlinear effects of the COVID-19 shock. We find that our model—compared with a benchmark model—has a significant increase in prediction accuracy, measured as a 15 to 45 percent reduction in forecast error.

Stochastic spectral finite element models of practical engineering systems may involve solutions of linear systems or linearized systems for non-linear problems with billions of unknowns. For stochastic modeling, it is therefore essential to design robust, parallel and scalable algorithms that can efficiently utilize high-performance computing to tackle such large-scale systems. Domain decomposition based iterative solvers can handle such systems. Although these algorithms exhibit excellent scalabilities, significant algorithmic and implementational challenges exist to extend them to solve extreme-scale stochastic systems using emerging computing platforms. Intrusive polynomial chaos expansion based domain decomposition algorithms are extended here to concurrently handle high resolution in both spatial and stochastic domains using an in-house implementation. Sparse iterative solvers with efficient preconditioners are employed to solve the resulting global and subdomain level local systems through multi-level iterative solvers. Parallel sparse matrix–vector operations are used to reduce the floating-point operations and memory requirements. Numerical and parallel scalabilities of these algorithms are presented for the diffusion equation having spatially varying diffusion coefficient modeled by a non-Gaussian stochastic process. Scalability of the solvers with respect to the number of random variables is also investigated.

Primary spindle cell sarcoma of the left atrium is an extremely rare tumour. Surgical excision is the mainstay of treatment since it responds poorly to chemotherapy or radiotherapy. In spite of all the treatment, the prognosis remains poor due to inadvertent delay in diagnosis, few therapeutic options and propensity to metastasize. We present a 47-year-old male who underwent a surgical excision of a left atrial mass in February 2010. It was proved to be a high-grade spindle cell sarcoma on histopathology. He presented again in October 2010 with recurrence of the tumour for which he was re-operated. However, the tumour recurred again within one month, to which the patient succumbed.

Introduction: There have been biological findings alluding to the role of selective COX-2 inhibitors in reducing the risk of developing cancers including breast cancer. Epidemiological data suggest that COX-2 inhibitors may prevent the development of cancers, including colorectal, esophageal and lung cancer. To date, there exists no study that reported any relationship between their use and the risk of breast cancer recurrence. Thus, we investigated the associations between the use of COX-2 inhibitors and the risk of tumor recurrence among breast cancer patients. Methods: We reviewed the medical records of female patients diagnosed with stage II/III breast cancer in Albert Einstein Medical Center between 1999 and 2005 and later reached no evident disease (NED) stage. Follow up period was from 1999 to 2008 with mean and maximum being 4.4 years and 9.8 years, respectively. COX-2 inhibitor user was defined as patients taking the medication in NED stage for at least 6 months. Age, race, family history, smoking history, menopausal status, diabetes, HER2/Neu status, hormone receptor status, TNM stage, histology, and treatment received were compared between COX-2 inhibitor users and nonusers. The primary outcome was disease free survival. The secondary outcome was overall survival. Multivariate analyses were performed using Cox proportional hazard model. Results: Of 682 cancer patients, 7.3 % (n=50) were prescribed COX-2 inhibitor; 10.0 % (5/50) of patients developed recurrence among COX-2 inhibitor users, while 22.0% (139/632) did among nonusers (chi-square test: P Conclusions: The use of COX-2 inhibitors seems to be related to a reduced risk of developing recurrence in patients with breast cancer. This result provides the clinical evidence, in accordance with existing biological data, that COX-2 inhibitor use in breast cancer patients may be inversely associated with tumor recurrence. However, prospective randomized trials are required to validate our finding. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P6-10-01.

Rotational atherectomy is contraindicated in dissected coronary arteries since it can lead to progression of the dissection or perforation. In our case, the right coronary artery (RCA) arose anomalously from the left coronary sinus. The lesion in the RCA was an undilatable calcified one. There was a dissection in the RCA due to high pressure balloon dilatation. Since the patient was hemodynamically unstable and there were no options besides rotablation, we proceeded with caution. Rotablation and stenting were successfully done. Our case report highlights the importance of the need for good guide catheter support even in the presence of anomalously arising arteries and the rotablation option for unyielding calcified coronary lesions, even in the setting of a dissection.

The present study focuses on the uncertainty quantification of an aeroelastic instability system. This is a classical dynamical system often used to model the flow induced oscillation of flexible structures such as turbine blades. It is relevant as a preliminary fluid-structure interaction model, successfully demonstrating the oscillation modes in blade rotor structures in attached flow conditions. The potential flow model used here is also significant because the modern turbine rotors are, in general, regulated in stall and pitch in order to avoid dynamic stall induced vibrations. Geometric nonlinearities are added to this model in order to consider the possibilities of large twisting of the blades. The resulting system shows Hopf and period-doubling bifurcations. Parametric uncertainties have been taken into account in order to consider modeling and measurement inaccuracies. A quadrature based spectral uncertainty tool called polynomial chaos expansion is used to quantify the propagation of uncertainty through the dynamical system of concern. The method is able to capture the bifurcations in the stochastic system with multiple uncertainties quite successfully. However, the periodic response realizations are prone to time degeneracy due to an increasing phase shifting between the realizations. In order to tackle the issue of degeneracy, a corrective algorithm using constant phase interpolation, which was developed earlier by one of the authors, is applied to the present aeroelastic problem. An interpolation of the oscillatory response is done at constant phases instead of constant time and that results in time independent accuracy levels. Copyright � 2013 by ASME.

Aeroelastic stability remains an important concern for the design of modern structures such as wind turbine rotors, more so with the use of increasingly flexible blades. A nonlinear aeroelastic system has been considered in the present study with parametric uncertainties. Uncertainties can occur due to any inherent randomness in the system or modeling limitations, and so forth. Uncertainties can play a significant role in the aeroelastic stability predictions in a nonlinear system. The analysis has been put in a stochastic framework, and the propagation of system uncertainties has been quantified in the aeroelastic response. A spectral uncertainty quantification tool called Polynomial Chaos Expansion has been used. A projection-based nonintrusive Polynomial Chaos approach is shown to be much faster than its classical Galerkin method based counterpart. Traditional Monte Carlo Simulation is used as a reference solution. Effect of system randomness on the bifurcation behavior and the flutter boundary has been presented. Stochastic bifurcation results and bifurcation of probability density functions are also discussed. Copyright � 2010 Ajit Desai and Sunetra Sarkar.

Introduction: Triple-negative breast cancer patients have a worse prognosis as compared to other subtypes of breast cancer. Patients are usually diagnosed at a younger age. Tumor size is usually larger with poorly differentiated histology and possible visceral metastasis. Several studies have been published on the risk factors for developing triple negative breast cancer. On the other hand, factors predicting mortality after diagnosis among triple negative breast cancer patients is still lacking. Objectives: The study aimed to describe the population of triple negative breast cancer patients diagnosed at Albert Einstein Medical Center from 2003–2007. Demographic data, risk factors and treatment interventions in this population were gathered. The measured outcomes were mortality and disease free survival. Methods: This study was a retrospective review of women diagnosed with triple negative breast cancer at the Albert Einstein Healthcare Network Cancer Center in 2003–2007. Patients diagnosed with cancer metastasized to the breast and causes of death other than from complications of cancer were excluded from the study. Statistical Analysis: Descriptive statistics and frequency distribution were used to categorize the characteristics of the subjects. Chi-square test was used for bivariate analysis. Logistic regression was used for multivariate analysis. Results: A total of 214 patients were diagnosed with triple negative breast cancer from 2003–2007. 114 (53%) were African Americans, 84 (39%) were Caucasians and 16 (7%) belonged to other racial groups. The mean age of diagnosis was 56.6 (SD 15.9) years old. At the end of the four year period, 48 (22.4%) of the 214 patients died. Using chi square test and logistic regression, patients who underwent chemotherapy were less likely to die (95% CI 0.14 – 0.83, p 0.02, OR 0.35). The patients with tumor size more than 2 cm or had history of ischemic heart disease had increased mortality (95% CI 1.36 – 8.04, p 0.01, OR 3.3 and 95% CI 1.29 – 49, p 0.03, respectively). Chemotherapy was significantly associated with disease free survival (95% CI 1.49 – 18.65, p 0.01, OR 5.27). There was no significant association between mortality or disease free survival to age and race. Conclusion: Early diagnosis and screening significantly affects treatment and survival outcome. Prompt chemotherapy may contribute to a patient's survival and disease free status. Tumor size more than 2 cms and the presence of ischemic heart disease were significant predictors of mortality outcome whereas age and race were not found to be associated with mortality or disease free survival. Citation Information: Cancer Prev Res 2010;3(1 Suppl):A132.

There have been epidemiologic and biochemical findings alluding to the role of angiotensin converting enzyme inhibitors(ACEIs) and angiotensin receptor blockers(ARBs) in reducing the risk of developing cancer. To date, there exists no study that reported any relationship between their use and the risk of cancer recurrence. Thus, we investigated the associations between the use of ACEI/ARB and the risk of tumor recurrence among breast cancer patients without any evidence of disease.We reviewed the medical records of female patients diagnosed with stage II/III breast cancer in Albert Einstein Medical Center between 1999 and 2005 and later reached no evident disease (NED) stage after curative therapy. Follow up period was from 1999 to 2008 with mean and maximum being 4.4 years and 9.8 years, respectively. ACEI/ARB user was defined as patients taking the medication in NED stage for at least 6 months. Multivariate analyses were performed using logistic regression and Cox proportional hazard model.Of 707 cancer patients and of 335 cancer patients with hypertension, 164, representing 23.3% of total and 49.0% of those with hypertension were prescribed ACEI/ARB. ACEI/ARB use was associated with older age (P Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3121.