Dhruv Mayank

ORCID: 0000-0003-1985-7488
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About
Contact & Profiles
Research Areas
  • Acute Ischemic Stroke Management
  • Advanced Text Analysis Techniques
  • Persona Design and Applications
  • Venous Thromboembolism Diagnosis and Management
  • Time Series Analysis and Forecasting
  • Traumatic Brain Injury and Neurovascular Disturbances
  • Text and Document Classification Technologies
  • Customer churn and segmentation
  • Sentiment Analysis and Opinion Mining

University of Toronto
2016-2017

Systems, Applications & Products in Data Processing (Canada)
2016

University of Calgary
2016

Alberta Children's Hospital
2016

<h3>SUMMARY:</h3> The concept of Nash equilibrium, developed by John Forbes Jr, states that an equilibrium in noncooperative games is reached when each player takes the best action for himself or herself, taking into account actions other players. We apply this to provision endovascular thrombectomy treatment acute ischemic stroke and suggest collaboration among hospitals a health care jurisdiction could result practices such as shared call pools neurointervention teams, leading better...

10.3174/ajnr.a5481 article EN cc-by American Journal of Neuroradiology 2017-11-30

Social networks are now a primary source for news and opinions on topics ranging from sports to politics. Analyzing with an associated sentiment is crucial the success of any campaign (product, marketing, or political). However, there two significant challenges that need be overcome. First, social produce large volumes data at high velocities. Using traditional (semi-) manual methods gather training is, therefore, impractical expensive. Second, humans express more than emotions, typical...

10.1109/icdmw.2016.0139 article EN 2016-12-01

This paper presents a novel big data analytics framework for creating explainable personas retail and business banking customers. These are essential to better tailor financial products improve customer retention. is comprised of several components including anomaly detection, binning aggregation contextual data, clustering transaction time series, mining association rules that map cluster identifiers. Leveraging rich available from nearly 60,000 90,000 customers institution, we empirically...

10.1109/bigdata50022.2020.9378483 article EN 2021 IEEE International Conference on Big Data (Big Data) 2020-12-10
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