ABOUT

Senior data science professional with a robust background in statistical analysis and data modeling obtained over five years of applied research and relevant work. Goal-driven, great listener, strong communicator, capable of working individually or as part of a team.

JOURNAL PUBLICATIONs: https://bit.ly/HNPublications (Google Scholar)

CONTACT

EMAIL: Nouraei@gmail.com
Linkedin: https://www.linkedin.com/in/hooman-nouraei/

PROJECT 1

Clustering of Heart Failure With Preserved Ejection Fraction

Description: Heart failure with preserved ejection fraction (HFpEF) is a clinical syndrome in need of improved phenotypic classification. The objective was to evaluate whether unbiased clustering analysis using dense phenotypic data could identify phenotypically distinct HFpEF categories.

In this project, I focused on developing a high-density phenotypic classification of clinical cardiovascular syndrome. I modeled unbiased cluster analysis of dense phenotypic data from multiple domains in meaningful, clinically relevant categories of patients with HFpEF with significant differences in underlying etiology/ pathophysiology and differential risk of adverse outcomes. I applied unsupervised clustering algorithms (Agglomerative clustering, and K-means) on cardiovascular syndrome data. I identified 6 clinically relevant HFpEF patient categories. The findings of this project will be implemented in developing targeted therapies and also in designing future clinical trials, thereby improving the track record of HFpEF clinical trials.