Elham Rastegari, PhD

Assistant Professor

Contact

ElhamRastegari@creighton.edu
Heider College of Business
Accounting and Business Intelligence & Analytics - Business
HARP - Harper Center for Student Life & Learn - 4029 B

Elham Rastegari, PhD

Assistant Professor

Elham Rastegari, Ph.D. is an Assistant Professor of Business Intelligence and Analytics at Creighton University. She received her Ph.D. from the college of Information Science and Technology- department of Biomedical Informatics at the University of Nebraska. Elham's Master of Science and Bachelor of science degrees are both in Computer Science and Engineering and her research focus is promoting healthcare by advancing healthcare decision-making process using wireless technologies and data from social media.

Teaching Interests

  • Research Assistant

Research Focus

Social Computing
Promoting Decision-making in Healthcare Domain
Business Intelligence Systems in Healthcare

Department

Accounting and Business Intelligence & Analytics

Position

Assistant Professor

Publications

  • Journal of statistics and data science education
    Gerhart Natalie, Analytics: What Do Business Majors Need and Where Do They Get It?, p. 1 - 48 2024
  • Surgeries
    Rastegari Elham, An innovative comparative analysis approach for the assessment of laparoscopic surgical skills
    4:1, p. 46 - 57 2023
  • Surgical innovation
    Rastegari Elham, Assessing Laparoscopic Surgical Skills Using Similarity Network Models: A Pilot Study
    28:5, p. 600 - 610 2021
  • Smart Health
    Rastegari Elham, A bag-of-words feature engineering approach for assessing health conditions using accelerometer data
    16, p. 100116 - 2020
  • ACM transactions on social computing
    Rastegari Elham, A Socio-Contextual Approach in Automated Detection of Public Cyberbullying on Twitter
    1:4, p. 1 - 22 2018
  • International Journal of Health and Medical Engineering
    Rastegari Elham, Pervasive Computing in Healthcare
    511, p. 1166 - 1172 2011
  • Rastegari Elham, Determining the Temporal Factors of Survival Associated with Brain and Nervous System Cancer Patients: A Hybrid Machine Learning Methodology -0001

Other

  • Longitudinal Assessment of Simulated Surgical Training Using Similarity Network Models

  • Longitudinal Assessment of Simulated Surgical Training Using Similarity Network Models