Portfolio of Asif rahman
Data Science and Analytics | MS in Industrial Engineering | 4 + Years of U.S. Experience
Embedded Files
Portfolio of Asif rahman
Welcome
Welcome
Asif rahman, Ms
Asif rahman, Ms
I am a passionate data professional with over 4 years of U.S. experience in data analytics, machine learning, and project management. Holding a Master’s degree in Industrial and Systems Engineering with a specialization in Data Science and Analytics from West Virginia University, I have led impactful projects in healthcare data analytics and Machine Learning based predictive modeling. My published research with WVU Medicine, spanning studies on sleep apnea, pulmonary hypertension, and the effects of COVID-19 on mental health, reflects my commitment to leveraging data to solve real-world problems, inspire innovation, and create lasting value. I am driven by the belief that data holds the power to inform, inspire, and transform decision-making.
SELECTED PROJECTS
SELECTED PROJECTS
Project Title: Changes in Psychiatric Diagnosis Associated With SARS-CoV-2 Infection and Predicting the Development of New Psychiatric Illness in COVID Patients by Using Machine Learning Approach: A Study Using the US National COVID Cohort Collaborative (N3C)
NIH Project
Summary:
This project investigates the link between COVID-19 infection and new-onset psychiatric disorders, particularly schizophrenia spectrum and psychotic disorders, using data from the U.S. National COVID Cohort Collaborative (N3C). I built and compared multiple machine learning models — including Random Forest, Logistic Regression, and Decision Tree — alongside Cox Proportional Hazards survival analysis to identify key clinical and demographic predictors of psychiatric risk following COVID-19. The findings established a statistically significant association between infection and psychiatric outcomes, supporting the case for proactive mental health screening in COVID-19 recovery care.
Project Title: Geospatial and Temporal Analysis of Pediatric Non-Accidental Trauma in Rural Appalachia
Summary:
This project examines 14 years of pediatric emergency department data to understand how the COVID-19 pandemic reshaped patterns of child physical abuse in a rural healthcare system. Using Python and GeoPandas, I built statistical models and custom geospatial maps that revealed a striking, sustained increase in cases beginning with the pandemic and a strong association with community-level economic distress. The analysis highlights how local health systems can use their own data to identify at-risk populations and inform targeted intervention.
Project Title: Group-Based Trajectory Modeling of Longitudinal Inflammatory Biomarker Patterns
Summary:
This project applies Group-Based Trajectory Modeling in R to classify how critically ill patients' inflammatory biomarker levels evolve over time, rather than relying on a single snapshot measurement. I fit and compared latent class mixed models across multiple candidate solutions, selecting a final three-class model that balanced statistical fit with clinical interpretability. The resulting patient subgroups — each following a distinct inflammatory trajectory — demonstrate how longitudinal modeling can surface risk patterns that static lab values alone would miss.
Project Title: Heart Failure Survival Analysis Dashboard
Summary:
This interactive Tableau dashboard analyzes survival trends and key risk factors — including anemia, diabetes, smoking status, and blood pressure — in a clinical heart failure patient cohort. Designed for clinical and non-technical stakeholders alike, the dashboard enables users to explore patient risk patterns without needing any coding or statistical background. It reflects my focus on making complex clinical data accessible and actionable at the point of care.
Get In Touch
Get In Touch
Page updated
Google Sites
Report abuse