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Arjun Verma on Muck Rack

Arjun Verma

San Jose
As seen in: PLOS ONE, Cell Press
US Immigration Law Firm, the Verma Law Firm provides legal services exclusively in the areas of business immigration and family immigration law.

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Arjun Verma’s Biography

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Verma Law Firm has been providing legal services exclusively in employment and family immigration over the past 14 years. They charge a flat fee for all our cases and provide a very reasonable payment plan. Their Silicon Valley immigration attorneys have been providing excellent personalized immigration services to various individual and corporate clients. Their legal team places a great emphasis and importance on personalized services, making it a point to be there for clients throughout every…

Articles

Acute clinical and financial outcomes of esophagectomy at safety-net hospitals in the United States

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By Sara Sakowitz, Russyan Mark Mabeza, Syed Shahyan Bakhtiyar, Arjun Verma
| PLOS ONE
Loading metrics Open Access Peer-reviewed Research Article Abstract Background While safety-net hospitals (SNH) play a critical role in the care of underserved communities, they have been associated with inferior postoperative outcomes. This study evaluated the association of hospital safety-net status with clinical and financial outcomes following esophagectomy.
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Association of hospital volume with conversion to open from minimally invasive colectomy in patients with diverticulitis: A national analysis

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By Shayan Ebrahimian, Arjun Verma, Sara Sakowitz, Manuel Olmedo
| PLOS ONE
Loading metrics Open Access Peer-reviewed Research Article Abstract Background Despite the known advantages of minimally invasive surgery (MIS) for diverticular disease, the impact of conversions to open (CtO) colectomy remains understudied. The present study used a nationally representative database to characterize risk factors and outcomes associated with CtO in patients with diverticular disease.
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Machine learning-based modeling of acute respiratory failure following emergency general surgery operations

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By Peyman Benharash, Joseph Hadaya, Arjun Verma, Yas Sanaiha, Ramin Ramezani
| PLOS
Model development strategy We developed 4 distinct models to predict PRF–XGBoost complete, LR complete, XGBoost sparse, LR sparse. The complete models contain the maximum number of covariates necessary to yield the highest predictive performance. In contrast, the sparse models had a limited number of input features and were designed to be more portable for use in the clinical setting.
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