Biography
Dr. Vinod Kumar Chauhan is a Postdoctoral Research Assistant in Healthcare with CHI Lab and currently, working on PARADISE project. He is developing predictive deep learning based algorithms for post-operative atrial fibrillation in patients undergoing cardiac surgery using ICU data.
Before joining CHI lab, Vinod was a Research Associate in Industrial Machine Learning at Institute for Manufacturing, Department of Engineering, University of Cambridge UK. At Cambridge, he applied network science, machine learning and mathematical modelling to solve industrial problems. He also won (shared with one another) Institute for Manufacturing Research Execellence Award 2021.
He did his PhD from Panjab University Chandigarh, India and worked on developing optimisation algorithms to solve large-scale machine learning problems.
Most Recent Publications
Comparing the risks of new-onset gastric cancer or gastric diseases in type 2 diabetes mellitus patients exposed to SGLT2I, DPP4I or GLP1A: a population-based cohort study
Comparing the risks of new-onset gastric cancer or gastric diseases in type 2 diabetes mellitus patients exposed to SGLT2I, DPP4I or GLP1A: a population-based cohort study
Improving Diagnostics with Deep Forest Applied to Electronic Health Records
Improving Diagnostics with Deep Forest Applied to Electronic Health Records
Network science approach for identifying disruptive elements of an airline
Network science approach for identifying disruptive elements of an airline
Synthesizing Mixed-type Electronic Health Records using Diffusion Models
Synthesizing Mixed-type Electronic Health Records using Diffusion Models
Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing
Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing
Research Interests
AI for Healthcare
Current Projects
PARADISE: Developing predictive deep learning based algorithms for post-operative atrial fibrillation in patients undergoing cardiac surgery using ICU data.
Research Groups
Related Academics
Most Recent Publications
Comparing the risks of new-onset gastric cancer or gastric diseases in type 2 diabetes mellitus patients exposed to SGLT2I, DPP4I or GLP1A: a population-based cohort study
Comparing the risks of new-onset gastric cancer or gastric diseases in type 2 diabetes mellitus patients exposed to SGLT2I, DPP4I or GLP1A: a population-based cohort study
Improving Diagnostics with Deep Forest Applied to Electronic Health Records
Improving Diagnostics with Deep Forest Applied to Electronic Health Records
Network science approach for identifying disruptive elements of an airline
Network science approach for identifying disruptive elements of an airline
Synthesizing Mixed-type Electronic Health Records using Diffusion Models
Synthesizing Mixed-type Electronic Health Records using Diffusion Models
Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing
Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing
Most Recent Publications
Comparing the risks of new-onset gastric cancer or gastric diseases in type 2 diabetes mellitus patients exposed to SGLT2I, DPP4I or GLP1A: a population-based cohort study
Comparing the risks of new-onset gastric cancer or gastric diseases in type 2 diabetes mellitus patients exposed to SGLT2I, DPP4I or GLP1A: a population-based cohort study
Improving Diagnostics with Deep Forest Applied to Electronic Health Records
Improving Diagnostics with Deep Forest Applied to Electronic Health Records
Network science approach for identifying disruptive elements of an airline
Network science approach for identifying disruptive elements of an airline
Synthesizing Mixed-type Electronic Health Records using Diffusion Models
Synthesizing Mixed-type Electronic Health Records using Diffusion Models
Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing
Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing
