HOSSEIN ESTIRI, PHD, MS

Hossein Estiri, PhD, is a research fellow with the Laboratory of Computer Science and an Informatics training fellow of the National Library of Medicine. Dr. Estiri’s research involves designing data-driven systems for clinical decision making and healthcare policy. His recent work has focused on designing and developing visual analytics programs (VET, DQe-c, DQe-v, and DQe-p) to explore data quality in Electronic Health Records data. His research with  LCS is focused on applying Statistical Learning techniques (Deep Learning and unsupervised clustering) and Data Science methodologies to design systems that characterize patients and evaluate EHR data quality.

Dr. Estiri holds a PhD in Urban Planning and a PhD track in Statistics from University of Washington. Prior to joining LCS, Dr. Estiri completed a 2-year postdoctoral fellowship with the University of Washington’s Institute of Translational Health Sciences and Department of Biomedical Informatics. 

DQe-p prototype: unsupervised clustering of abnormal values in clinical observations. [work in progress]

DQe-p prototype: unsupervised clustering of abnormal values in clinical observations. [work in progress]