Ahmad Aljadaan, PhD

Curious researcher, machine learning scientist, and data scientist. Exceptional passion for applied research in Artificial Intelligence and Data Science to deduce insights from data and use them to make a positive impact. Great experience with Research and Innovation, Artificial Intelligence, Data Science, Project Management, Strategy Development, and Business Analysis. Created prediction model for readmissions that helped improve patient health outcome and reduce cost on hospitals.

National Health Lab for Emerging Technology, Ideathon 2019: Best Idea in the "Chronic Disease" Track

September 2019, Saudi Health Council

University of Washington Business Plan Competition: Perkins Coie Best Innovation/Technology Idea

May 2016­­, University of Washington

AMIA Student Design Competition (2nd Place)

November 2014, American Medical Informatics Association

• Improving the heart failure patient discharge decision-making process for the University of Washington Medical System.
• Queried with SQL and Cleaned via R 260,776 Electronic Health Record data points from the University of Washington Medical System.
• Built a tool that would help researchers automate the process of building an appropriate visit table and reduce the need for time-consuming chart review.
• Applied different Machine Learning Algorithms such as Logistic Regression, Random Forest, and LASSO to predict very early readmission (shorter than 30 days) based on patient characteristics at the time of discharge using R.
• Applied missing not at random imputation technique in R to deal with missing data.
• Identified 3 subgroups for readmission within 30 days where very early readmission occurs within the first 7 days
• 40% of the 30 days heart failure readmission occurs within the first 7 days; high cost and increase the risk of mortality.
• Currently embedding these prediction models into a good effective decision support tool in the UW Medical Center.