George Mason University
George Mason University Mason
George Mason University

Janusz Wojtusiak, PhD

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Dr. Wojtusiak, Associate Professor of Health Informatics and Director of the Machine Learning and Inference Laboratory, has research expertise that includes machine learning, health informatics, artificial intelligence in clinical decision support and knowledge discovery in medical data, machine learning, evolutionary computation, intelligent evolutionary design, knowledge mining, health data analytics, and a wide range of applications of these fields in health care. His particular area of interest is in developing algorithms that derive simple and transparent models from complex healthcare data.

Dr. Wojtusiak serves as the Division Director for Health Informatics in the Department of Health Administration and Policy, and is the primary advisor of PhD, Health Services Research students in the Knowledge Discovery and Health Informatics concentration. He teaches HAP 618 (Computational Methods in Health Informatics), HAP 730 (Healthcare Decision Analysis), HAP 752 (Advanced Health Information Systems), and HAP 780 (Healthcare Data Mining) at the graduate level.

He authored or co-authored over 70 research publications and presentations and continues to collaborate with multiple national and international institutions including University of Louisville, University of Bremen, Germany, and AGH University of Science and Technology. In 2007, he was a fellow of Hanse-Wissenschaftskolleg (Hanse Institute for Advanced Study), Germany and was a post-doctoral fellow at Mason after completing his Ph.D.

Selected Community Service

  • National Science Foundation proposal reviewer and panelist in the Division of Intelligent and Information Systems
  • Polish Science Foundation proposal reviewer in Machine Learning
  • Kentucky Science Foundation proposal reviewer
  • Editorial Board, Computer Science Journal, AGH press
  • Associate editor, Encyclopedia of the Sciences of Learning, Springer, 2011
  • Editor, Reports of the Machine Learning and Inference Laboratory, George Mason University
  • Area and Grand Prize judge at Fairfax County Regional Science Fair


PhD, Computational Sciences and Informatics, George Mason University (2007)

MS, Computer Science, Jagiellonian University, Krakow, Poland (2001)


Research Interests

  • Health Informatics
  • Artificial Intelligence
  • Clinical decision support
  • Healthcare knowledge discovery 


ElRafey, A., & Wojtusiak, J. (2017). Recent advances in scaling‐down sampling methods in machine learning. Wiley Interdisciplinary Reviews: Computational Statistics.

Min, H., Avramovic, S., Wojtusiak, J., Khosla, R., Fletcher, R.D., Alemi, F., & Elfadel, K.R. (2017). A comprehensive multimorbidity index for predicting mortality in intensive care unit patients. Journal of palliative medicine, 1; 20(1),35-41.

Min, H., Mobahi, H., Irvin, K., Avramovic, S., & Wojtusiak, J. (2017). Predicting activities of daily living for cancer patients using an ontology-guided machine learning methodology. Journal of Biomedical Semantics, 16; 8(1),39.

Kheirbek, R., Wojtusiak, J., Alemi, F., & Vlaicu, S. (2016). Lack of evidence for racial disparity in 30-day all-cause readmission rate for older US veterans hospitalized with heart failure. Quality Management in Health Care, 25(4), 191 196.

Wojtusiak, J., Alemi, F., Levy, C., & Williams, A. (2016). Predicting functional decline and recovery following hospitalization of residents in veterans affairs nursing homes. The Gerontologist, 56 (1), 42-51.

Levy, C., Zargoush, M., Williams, A., Williams, A.R., Giang, P., Wojtusiak, J., Kheirbek, R., & Alemi, F. (2016). Sequence of functional loss and recovery in nursing homes. The Gerontologist, 56 (1).

Levy C., Alemi F., Williams A.E., Williams A.R., Wojtusiak J., Sutton B., P Giang, Pracht, E., & Argyros, L. (2015). Shared homes as an alternative to nursing home care: impact of VA’s medical foster home program on hospitalization. The Gerontologist, 56(1).

Helmchen, L.A., Burke, M.E., Wojtusiak, J. (2015). Designing highly reliable adverse-event detection systems to predict subsequent claims. Journal of Healthcare Risk Management, 34(4):7-17.

Levy, C., Kheirbek, R., Alemi, F., Wojtusiak, J., Sutton, B., Williams, A.R., Williams, A. (2015). Predictors of 6-month mortality among nursing home residents: diagnoses maybe more predictive than functional disability. Journal of Palliative Medicine, 18(2):100-6.

Ngufor, C., & Wojtusiak, J. (2014). Learning from large distributed data: a scaling down sampling scheme for efficient data processing. International Journal of Machine Learning and Computing (IJMLC), 4(3), 216-224.

Domanski, P.A., Brown, J.S., Heo, J., Wojtusiak, J., & McLinden, M.O. (2014). “A thermodynamic analysis of refrigerants: Performance limits of the vapor compression cycle,” International Journal of Refrigeration, 38, 71-79.

Ngufor, C., & Wojtusiak, J. (2013). Unsupervised labeling of data for supervised learning and its application to medical claims prediction. Computer Science Journal, AGH Press, 14, 2, 191-214.

Wojtusiak, J., Warden, T., & Herzog, O. (2012). Machine learning in agent-based stochastic simulation: Inferential theory and evaluation in transportation logistics. Computers & Mathematics with Applications, 64, 12, 3658-3665.

Wojtusiak, J., Warden, T., & Herzog, O. (2012). The learnable evolution model in agent-based delivery optimization. Memetic Computing, 4, 3, 165-181.

Yashar, D., Wojtusiak, J., Kaufman, K., & Domanski, P.A. (2012). A dual mode evolutionary algorithm for designing optimized refrigerant circuitries for finned tube heat exchangers. HVAC&R Research, 18, 5, 834-844.

Michalski, R. S., & Wojtusiak, J. (2012). Reasoning with missing, not-applicable and irrelevant meta-values in concept learning and pattern discovery. Journal of Intelligent Information Systems, 39, 141-166, Springer.

Wojtusiak, J., Gewa, C.A., & Pawloski, L.A. (2011). Dietary assessment in Africa: integration with innovative technology,” African Journal of Food, Agriculture, Nutrition, and Development, 11, 7.

Landon, B.E. , Reschovsky, J.D. , Pham, H.H., Kitsantas, P., Wojtusiak, J., & Hadley, J. (2009). Creating a parsimonious typology of physician financial incentives," Health Services and Outcomes Research Methodology, 9, 219-233.

Wojtusiak, J., Chorowski, J., Pietrzykowski, J., & Zurada, J. M. (2009). Searching and reasoning with distributed resources in computational intelligence and machine learning. Journal of Applied Computer Science Methods, 1, 2.

Wojtusiak, J., Michalski, R. S., Simanivanh, T., & Baranova, A. V. (2009). Towards application of rule learning to the meta-analysis of clinical data: An example of the metabolic syndrome. International Journal of Medical Informatics, 4, 1, pp. 43-54.

Wojtusiak, J. (2009). The LEM3 system for multitype evolutionary optimization. Computing and Informatics, 28, pp. 225-236.

Zurada, J. M., Mazurowski, M.A., Abdullin, A., Ragade, R., Wojtusiak, J., & Gentle, J. E. (2009). Building virtual community in computational intelligence and machine learning. Computational Intelligence Magazine, 4, 1, pp. 43-54.

Wojtusiak, J. & Michalski, R. S. (2008). Analyzing diaries for analytical relapse prevention using natural induction: A method and preliminary results," Quality Management in Health Care, 17.

Wojtusiak, J. (2007). Handling constrained optimization problems and using constructive induction to improve representation spaces in learnable evolution model. SIGEVOlution, Dissertation Corner, 2(3), 24-25.


Logistic Management Institute, October 1, 2015 – September 30, 2016
Integrating Complex Health Data for Analytics
Role: PI

Jeffers Foundation, August 31, 2015 – August 30, 2016
Applying the Ontology-guided Machine Learning to analyze the Surveillance, Epidemiology, and End Results Program-Medicare Health Outcomes Survey (SEER-MHOS) Linked Database,
PI: Hua Min, co-PI: Janusz Wojtusiak

National Institute of Standards and Technology/Dakota Consulting, August 18, 2014 – August 17, 2015
Expanded Capabilities for EVAP-COND Heat Exchanger Design Tool Engineering Laboratory
Position: Principal Investigator

Department of Veterans Affairs/Green Technologies, October 1, 2013 – Sept. 30, 2014
Data Analysis for Artificial Intelligence
Position: Principal Investigator
Description: The project concerns analyzing data to improve quality of care in the VA. It consists of three main subprojects: ICU mortality index, causes and prediction of heart failure readmissions, and home care quality and cost analysis.

National Institute of Standards and Technology, September 1, 2013 – February 28, 2014
Implementation of Dual Evaporator-Condenser Mode in Intelligent Evolutionary Optimization system ISHED
Position: Principal Investigator

American Society for Healthcare Risk Management, January 1, 2013–December 31, 2013
Designing Highly Reliable Adverse-Event Detection Systems
Position: co-PI
Principal Investigator: Lorens Helmchen
Description: This project seeks to combine and analyze existing risk‐management databases at a large hospital system in Northern Virginia to improve the accuracy and efficiency of adverse event reporting and detection systems. Machine learning methods are used to predict outcomes of submitted reports.

American Cancer Society
Patient-Centered Medical Homes and Care of Cancer Survivors
Position: co-PI
Principal Investigator: Jack Hadley
Description: The primary objective of the study is to measure the extent to which primary care medical practices (both those recognized as PCMHs and others) apply key elements of the model in caring for cancer survivors, electronic monitoring of medication regimens, family engagement and active discussion and consideration of end of life care.

Robert Wood Johnson Foundation, October 1, 2012 – September 30, 2014
Medicare Payments, Market Structure, and Choice of Initial Management Strategy for Prostate Cancer in Medicare Fee-for-Service Beneficiaries
Position: Co-PI
Principal Investigator: Jack Hadley

Department of Veterans Affairs, December 1, 2011 – September 30, 2015
Evaluation of Medical Foster Homes
Position: GMU PI, IPA

National Institute of Standards and Technology, September 1, 2011 – February 28, 2013
Simplicity-based and Dual Optimization in the ISHED System
Position: Principal Investigator

Cochrane Collaboration Fellowship September 2011 – May, 2012
Federally Qualifying Health Clinics: Systematic Review Using Traditional and Machine Learning Based Methods
Position: Principal Investigator (co-investigator with John Cantiello)

George Mason University Summer Faculty Research, June – August 2011
Literature-based Individualized Comparative Effectiveness
Position: Principal Investigator
Mason-Inova Fund, September 1, 2010 – August 31, 2012
Development and Testing of Artificial Intelligence Application for Healthcare Financial Management
Position: Principal Investigator (co-PI John Shiver)
Description: The project uses computational methods to investigate disparities between billed and paid amounts in a hospital system. The project’s goal is to detect regularities in unpaid or partially paid bills, and create a screening mechanism for early detection of potential non-payments.

National Institute of Standards and Technology, 60NANB9D9151, September 1, 2010 – August 30, 2011
Intelligent Evolutionary Design: New Algorithms in ISHED and EVAP-COND Systems
Position: Principal Investigator
Description: Continuation and extension of the pilot successful project. The project investigates an intelligent optimization method and its application in engineering design. Although not directly healthcare related, the project is based on the same methodology as other projects. The project funds development of software and methods that are used also in healthcare related projects (i.e., comparative effectiveness, decision support).

National Institute of Standards and Technology, 60NANB9D9151, July 1, 2009 – June 30, 2010 (extension until August 30, 2010)
Investigation and Development of New Features in Intelligent Heat Exchanger Design
Position: Principal Investigator
Description: The project investigates an intelligent optimization method and its application in engineering design. Although not directly healthcare related, the project is based on the same methodology as other projects. The project funds development of software and methods that are used also in healthcare related projects (i.e., financial data management, comparative effectiveness, decision support).

National Science Foundation, CBET 0742487, October 1, 2007 – September 30, 2008
Computational Intelligence and Machine Learning Virtual Infrastructure Network
Position: Researcher, developed a method for collaboration with healthcare community, participated in development of the project web portal.
Principal Investigator: Jacek M. Zurada & James Gentle
Description: The goal of the project is to create infrastructure for collaboration within the computational intelligence and machine learning community, and with researchers, students, and practitioners from other disciplines. The primary focus is in collaboration with the healthcare community.

National Security Agency, LUCITE #32, February 10, 2003 – February 9, 2004
Learning User Behavior and Understanding Style: The Natural Induction Approach
Position: Graduate Research Assistant, co-developed software and methodology, performed experimental evaluation of the method.
Principal Investigator: Ryszard S. Michalski
Description: The project investigates an intelligent method able to detect intruders in computer systems and misuse of data and computer systems.

National Science Foundation, IIS 0097476, December 31, 2001 – August 31, 2007
Non-Darwinian Evolutionary Computation: Guiding Evolution by Machine Learning
Position: Graduate Research Assistant, co-developed methodology and software in the project. PhD dissertation related to the project.
Principal Investigator: Ryszard S. Michalski
Description: The project investigates an intelligent evolutionary optimization method. The method is particularly suitable for hard optimizations problems.

National Science Foundation, IIS 9906858, May 31, 2001 – September 30, 2007.
Inductive Databases and Knowledge Scouts
Position: Graduate Research Assistant, implemented software, co-developed and published several methods related to the project.
Principal Investigator: Ryszard S. Michalski
Description: The project investigates database systems capable of inferring plausible answers to queries for which no explicit data is available.

Honors & Awards

Shirley S. Travis Habit of Excellence Award, 2016, College of Health and Human Services, George Mason Univeristy

Award for Outstanding Doctoral Work, 2008, George Mason University Department of Computational and Data Sciences

Best poster presentation Award, 2007, Sixth International Conference on Machine Learning and Applications

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