Umit Tokac, PhD

Umit Tokac, PhD is an Associate Professor of Data Science at the University of Missouri–St. Louis College of Nursing and a data scientist at the National Heart, Lung, and Blood Institute through the NIH Data Scholar Program. He holds a PhD in Measurement and Statistics from Florida State University and brings interdisciplinary expertise spanning data science, artificial intelligence, and health research. His work focuses on machine learning, geospatial modeling, and AI-driven methods to address public health challenges, including climate-related cardiopulmonary outcomes, personalized mental health treatment, and surgical risk prediction. Dr. Tokac has led federally and internationally recognized collaborations and is committed to research that advances health equity and social good. An award-winning educator, he teaches graduate-level biostatistics and data science using inclusive, active learning approaches and mentors students across disciplines.

Education

PhD, Florida State University
MS, Florida State University
MS, Gazi University (Turkey)
BS, Gazi University (Turkey)

Research Interests

• Machine Learning and Artificial Intelligence for Health Development and application of supervised and unsupervised learning methods to model complex health outcomes, personalize treatment, and support clinical decision-making.
• Climate, Environmental, and Geospatial Data Science Spatiotemporal modeling of climate and environmental exposures (e.g., wildfires, extreme heat) and their effects on cardiopulmonary and population health outcomes.
• Statistical Modeling and Measurement in Health Sciences Advanced regression, multilevel modeling, latent variable models, and psychometric methods for improving measurement quality and inference in health research.
• Natural Language Processing and Network Analysis Text mining and network-based approaches for analyzing clinical notes, patient narratives, and system-level health data.
• AI-Driven Precision Mental Health and Health Equity Algorithmic approaches to personalized mental health care, with a focus on fairness, bias mitigation, and equitable outcomes across populations.
• Data Science Education and Evidence-Based Pedagogy Innovative, inclusive approaches to teaching statistics and data science, including active learning and technology-enhanced instruction.

Selected Publications

• Tokac, U., McKeever, M., Werner, K. B., Aktar, M.* & Razon, S. (2025) Enhancing Biostatistics Skills in Online Nursing Education through Think-Pair-Share Approach. Nurse Educator, 00(0): 1-7.  https://www.tandfonline.com/doi/full/10.1080/10376178.2022.2147850
• Chipps, J., Cromhout, A., & Tokac, U. (2025). Using Machine Learning to Predict Resilience Among Nurses in a South African Setting.  International journal of environmental research and public health,  22(7), 996. https://doi.org/10.3390/ijerph22070996
• Zhao, D., Yao, J., Li, Y., Jacobs, D. R., Jr., Gao, T., Nie, L. H., Wu, C. O., Tokac, U., Li, S., Zhao, H., Lloyd-Jones, D., Hou, L., & Zhang, K. (2025). Urban imperviousness and carotid intima-medial thickness: Evidence from the Coronary Artery Risk Development in Young Adults (CARDIA) Study.  The Science of the total environment, 978, 179376. https://doi.org/10.1016/j.scitotenv.2025.179376
• Tokac, U. & Chipps, J. A., Brysiewicz, P., Bruce, J., Clarke, D. L. (2025) Using Natural Language Processing in the LACE Index Scoring Tool to predict unplanned trauma and surgical readmissions in South Africa. World journal of surgery, 49(4), 1067–1073. https://doi.org/10.1002/wjs.12523
• Tokac, U., Chipps, J., Brysiewicz, P., Bruce, J., & Clarke, D. (2025). Using machine learning to improve readmission risk in surgical patients in South Africa.  International journal of environmental research and public health, 22(3), 345. https://doi.org/10.3390/ijerph22030345
• Tokac, U., McKeever, M., & Razon, S. (2024). Mental health and exercise during the COVID-19 pandemic: A Twitter sentiment analysis. Journal of Health Psychology. 2024; 0(0). https://doi.org/10.1177/13591053241258208
• Gramke, L., Werner, K. B., & Tokac, U. (2024). Estimated risk for overactive bladder in Native American women: Unsupervised machine learning approach. Urologic Nursing, 44(1). https://doi.org/10.7257/2168-4626.2024.44.1.9
• Li, F., Gu, H., Tokac, U., Werner, K. B., Fish, A. F., & Lou, Q. (2024). A multi-sample path analysis including a novel psychological indicator attitude toward life after the first wave of COVID-2019. SAGE Open Nursing, 10. https://doi.org/10.1177/23779608231215580
• Falcone, M. L., Tokac, U., Fish, A. F., Van Stee, S. K., & Werner, K. B. (2023). Factor structure and construct validity of a hospital survey on patient safety culture using exploratory factor analysis. Journal of Patient Safety, 19(5), 323-330. https://doi.org/10.1097/PTS.0000000000001126
• Li, F., Tokac, U., Werner, K. B., Fish, A. F., & Lou, Q. (2022). Determination of key inflection points for maladaptive responses in US nurses during the pandemic: A network analysis. SAGE Open Nursing, 8, 1-10. https://doi.org/10.1177/23779608221140719
• Tokac, U., Brysiewicz, P., & Chipps, J. (2022). Public perceptions on Twitter about nurses during the pandemic: Twitter sentiment analysis. Contemporary Nurse. https://doi.org/10.1080/10376178.2022.2147850
• Tokac, U., & Razon, S. (2021). Nursing professionals' mental well-being and workplace impairment during the COVID-19 crisis: A network analysis. Journal of Nursing Management, 29(6), 1653-1659. https://doi.org/10.1111/jonm.13285
• Berger, C., Tokac, U., Fish, A. F., & Dane, J. N. (2020). Implementation science: Changing practice in oral health. The Nurse Practitioner, 45(7), 27-34. https://doi.org/10.1097/01.NPR.0000669132.47036.2c
• Freeman, A., Nam-Speers, J., & Tokac, U. (2020). A quantitative meta-analysis of organizational ownership and technical efficiency: Non-linear influence by facility types and time for nonprofit and for-profit healthcare providers. International Review of Public Administration, 25(2), 106-128. https://doi.org/10.1080/12294659.2020.1775330
• Tokac, U., Novak, E., & Thompson, G. C. (2019). Effects of game-based learning on students’ mathematics achievement: A meta-analysis. Journal of Computer Assisted Learning, 35, 407-420. https://doi.org/10.1111/jcal.12347

Selected Presentations

1. Tokac, U., McKeever, M., Werner, K. B., Aktar, M., & Razon, S. (2024, October 3). Enhancing biostatistics education in nursing through the think-pair-share approach: A shift towards collaborative learning [Paper presentation]. The 23rd annual Focus on Teaching and Technology Conference (FTTC). University of Missouri - St. Louis.

2. Li, F., Tokac, U., Lou, Q., & Fish, A. F. (2024, April 3). Factors related to positive clinical care outcomes during the trend to use smaller insulin pen needles in China [Paper presentation]. Annual Spring Research Day of Barnes-Jewish College, Goldfarb School of Nursing and Sigma Tau, St. Louis, Missouri, United States.

3. Li, F., Tokac, U., Lou, Q., & Fish, A. F. (2024, February 28 - March 2). A path analysis of insulin injection practice and clinical outcomes with highlights of practice change between 2010 and 2017 Chinese patients with type 2 diabetes [Paper presentation].