Skip to main content

College of
Health Sciences

Yirui (Iris) Hu

Assistant Professor
Department of Population Health Sciences

Henry Hood Center for Health Research Building
100 North Academy Avenue
Danville, PA 17822
Phone: 570-214-1913

Yirui (Iris) Hu, PhD

Research Interests

Dr. Hu has expertise in several applied statistical areas, such as predictive modeling using machine learning, causal inference in observational disease-based studies, advanced study designs, longitudinal data analysis, clustering and outlier detection. Her statistical expertise can obtain insights to inform internal decision-making using real world evidence from Electronic Health Records (EHR) data. Dr. Hu has collaborated with clinicians as co-Investigators to support grant preparation and manuscript submission. From 2017 to present, Dr. Hu has served as a member for Geisinger Scientific Review Committee to support protocol reviews. In 2019 spring, Dr. Hu has contributed a research methods course 'Introduction to Meta-Analyses in Medical Research'.

  • Study Design: Support statistical analysis plan and sample size estimation for interventional studies, including cluster randomized trials, and stepped-wedge designs.
  • Evidence Based Medicine: Contribute data-driven understandings from case report or Meta-Analyses to assist health care professionals towards investigating the safety and effectiveness between interventions; evaluating the association between risk factors and clinical outcomes.
  • Predictive Analytics: Leverage traditional statistical regression and machine learning algorithms to support decision making. The standard predictive modeling pipeline includes Data preprocessing, missing data imputation, feature selection, model evaluation (Support Vector Machine, Decision Tree, Random Forest, eXtreme Gradient Boosting, Neural Network), and final model implementation.
  • Group-Based Trajectory Modeling: Identify potential patient subgroups to enhance treatment effects, due to the heterogeneity of treatment effects in study population. To better classify individuals and identify unique attributes that are predictive of outcomes is an important objective in precision medicine.
  • Real World Evidence: Obtain insights from large repositories of structured and unstructured patient EHR data in observational disease-based studies. Investigate on the integration of genomic information into EHR to guide decision making and improve patient care.

Recent publications

  • Singh G, Hu Y, Jacobs S, Brown J, George J, Bermudez M, Ho K, Green JA, Kirchner HL, Chang AR. Post-discharge mortality and rehospitalization among participants in a comprehensive acute kidney injury rehabilitation program. Kidney360. 2021 Jan 1.
  • Hu Y, Hao Q, Zhang L, Ross J, Robishaw S, Noble C, Wu X, Zhang X. A systematic review and meta-analysis of clinical trials of neuraxial, intravenous, and inhalational anesthesia for external cephalic version. Anesthesia and analgesia. 2020 Dec;131(6):1800.
  • Jing L, Ulloa Cerna AE, Good CW, Sauers NM, Schneider G, Hartzel DN, Leader JB, Kirchner HL, Hu Y, Riviello DM, Stough JV. A machine learning approach to management of heart failure populations. Heart Failure. 2020 Jul 1;8(7):578-87.
  • Lent MR, Hu Y, Benotti PN, Petrick AT, Wood GC, Still CD, Kirchner HL. Demographic, clinical, and behavioral determinants of 7-year weight change trajectories in Roux-en-Y gastric bypass patients. Surgery for Obesity and Related Diseases. 2018 Nov 1;14(11):1680-5.
  • Hu Y, Hoover DR. Non-randomized and randomized stepped-wedge designs using an orthogonalized least squares framework. Statistical methods in medical research. 2018 Apr;27(4):1202-18.


PhD, Statistics, Rutgers University, 2016
MS, Statistics, Rutgers University, 2013
BS, Mathematical Statistics, Beijing Normal University, 2011
Content from General Links with modal content