Nasibeh Azadeh Fard
Assistant Professor
Department of Industrial and Systems Engineering
Kate Gleason College of Engineering
Data Analytics
Healthcare Systems
585-475-2151
Office Location
Nasibeh Azadeh Fard
Assistant Professor
Department of Industrial and Systems Engineering
Kate Gleason College of Engineering
Data Analytics
Healthcare Systems
Education
BS, Iran University of Science and Technology; MS, Ph.D., Virginia Tech
Bio
Dr. Nasibeh Azadeh-Fard received her B.S. in Information Technology Engineering from Iran University of Science and Technology, and her M.S. and Ph.D. in Industrial and Systems Engineering from Virginia Tech. From August 2016 to August 2019, she was a visiting professor in Industrial and Systems Engineering Department at RIT. Prior to joining RIT, she was a postdoctoral fellow at Clemson University, Risk Engineering and System Analytics Center, where she conducted research on risk analysis and risk mitigation action plans for American International Group (AIG) insurance company.
Dr. Azadeh-Fard’s main research areas include data analytics, predictive modeling, healthcare systems engineering, risk analysis, early warning systems, and performance measurement and analysis. Her work has been published in peer reviewed journals including Healthcare Management Science, European Journal of Medical Research, Frontiers in Artificial Intelligence, Machine Learning with Applications, and Safety Science.
585-475-2151
Areas of Expertise
Data Analytics
Healthcare Systems
Predictive Modeling
Select Scholarship
- Muchiri, S., Pakdil, F., Azadeh-Fard, N., An analysis of length of stay and readmissions of COPD patients in the US between 2010 and 2020, International Journal of Healthcare Management, 1-12, 2024.
- Pai, DR., Pakdil, F., Azadeh-Fard, N., Applications of Data Envelopment Analysis in Acute Care Hospitals: A Systematic Literature Review, 1984-2022, Healthcare Management Science, 1-29, 2024.
- Jahangiri, S., Abdollahi, M., Patil, R., Rashedi, E., Azadeh-Fard, N., An Inpatient Fall Risk Assessment Tool: Application of Machine Learning Models on Intrinsic and Extrinsic Risk Factors, Machine Learning with Applications, 15, 100519, 2024.
- Adhiya, J., Barghi, B., Azadeh-Fard, N., Predicting the Risk of Hospital Readmissions Using a Machine Learning Approach: A Case Study on Patients Undergoing Skin Procedures, Frontiers in Artificial Intelligence, 6, 1213378, 2024.
- Barghi, B., Azadeh-Fard, N., Predicting risk of sepsis, comparison between machine learning methods: a case study of a Virginia hospital, European Journal of Medical Research, 27 (1), 213, 2022.
- Muchiri, S., Azadeh-Fard, N., Pakdil, F., The Analysis of Hospital Readmission Rates After the Implementation of Hospital Readmissions Reduction Program, Journal of Patient Safety, 18 (3), 237-244, 2022.
- Azadeh-Fard, N., Ghaffarzadegan, N., Camelio, J., Can a patient’s in-hospital length of stay and mortality be explained by early-risk assessments?, PLoS ONE, 11(9), 2016.
- Azadeh-Fard, N., Schuh, A., Rashedi, E., Camelio, J., Risk Assessment of Occupational Injuries Using Accident Severity Grade, Safety Science, Volume 76, 2015.
- Bish, E., Azadeh-Fard, N., Steighner, L., Hall, K., Slonim, A., Proactive Risk Assessment of Surgical Site Infections in Ambulatory Surgery Centers, Journal of Patient Safety, 2014.
Currently Teaching
ISEE-561
Data Analytics and Predictive Modeling
3 Credits
In systems where parameters can vary, we often want to understand the effects that some variables exert on others and their impact on system performance. “Data Analytics and Predictive Modeling” describes a variety of machine learning and data analysis techniques that can be used to describe the interrelationships among such variables. In this course, we will examine these techniques in detail, including data cleansing processes, data clustering, associate analysis, linear regression analysis, classification methods, naïve Bayes, neural networks, random forests, variable screening methods, and variable transformations. Cases illustrating the use of these techniques in engineering applications will be developed and analyzed throughout the course.
ISEE-661
Data Analytics and Predictive Modeling
3 Credits
In systems where parameters can vary, we often want to understand the effects that some variables exert on others and their impact on system performance. “Data Analytics and Predictive Modeling” describes a variety of machine learning and data analysis techniques that can be used to describe the interrelationships among such variables. In this course, we will examine these techniques in detail, including data cleansing processes, data clustering, associate analysis, linear regression analysis, classification methods, naïve Bayes, neural networks, random forests, variable screening methods, and variable transformations. Cases illustrating the use of these techniques in engineering applications will be developed and analyzed throughout the course. Note: Students required to take ISEE-561 for credit may not take ISEE-661 for credit.
ISEE-752
Decision Analysis
3 Credits
This course presents the primary concepts of decision analysis. Topics important to the practical assessment of probability and preference information needed to implement decision analysis are considered. Decision models represented by a sequence of interrelated decisions, stochastic processes, and multiple criteria are also addressed. We cover EMV and Non-EMV decision-making concepts. Finally, the organizational use of decision analysis and its application in real-world case studies is presented.
ISEE-761
Forecasting Methods
3 Credits
Forecasting Methods will provide the engineering student with the skills necessary to perform data driven time series analysis from an engineering applications perspective. A process driven approach will be used covering the entire forecasting process from data preparation and pre-processing techniques to model selection, performance evaluation, and monitoring. A special emphasis will be placed on performance evaluation and improvement of models used to predict RIT energy demand and peak load days. The course will cover topics in data cleansing, data transformation, trend and seasonality analysis, smoothing techniques, regression analysis for forecasting, seasonal and non-seasonal ARIMA models, dynamic regression, neural networks and advanced modeling techniques for multivariate time series analysis. Lectures and assignments will focus on predicting RIT energy demand considering circuits with 2MW solar fields or similar data sets.