Dan Lizotte, PhD

Dr. Lizotte

Associate Professor

P:519.661.2111 ext: 86644


Research Cluster Membership

Research Interests

  • Professor Lizotte's research aims to adapt and improve reinforcement learning, machine learning, and statistical techniques so they can be applied to new sources of health data, and can in turn provide stakeholders with the best available evidence for non-myopic health decision making. He is particularly interested in problems involving multiple outcomes, causal inference, and outlier detection in the domains of public health and primary health care.


  • BCS (New Brunswick)
  • MSc, PhD in Computer Science (Alberta)

Recent Research Grants

  • Machine learning methodology for sequential decision support from largescale longitudinal data (NSERC; 2018-2024)
  • Reinforcement Learning Methodology for Decision Analysis and Support in Long-term Care (NSERC; 2021-2022)
  • Beyond Supervised Learning: Artificial Intelligence Tools to Help Public Health Stakeholders Serve Marginalized Populations (CIHR; 2019-2023)
  • Artificial Intelligence for Public Health (AI4PH) Training Platform [co-Applicant with lead Dr. Laura Rosella] (CIHR; 2022-2026)

 Publications (selected) 

  • Brent Davis, Dawn E. McKnight, Daniela Teodorescu, Anabel Quan-Haase, Rumi Chunara, Alona Fyshe, and Daniel J. Lizotte. (2022) “Quantifying Depression-Related Language on Social Media During the COVID-19 Pandemic”, International Journal of Population Data Science, 5(4). doi: 10.23889/ijpds.v5i4.1716.
  • Mayuri Mahendran, Daniel Lizotte, and Greta R. Bauer. Describing intersectional health outcomes: An evaluation of data analysis methods. Epidemiology, 2022. (To appear.)
  • Greta R. Bauer, Siobhan M. Churchill, Mayuri Mahendran, Chantel Walwyn, Daniel Lizotte, and Alma Angelica Villa-Rueda. Intersectionality in quantitative research: A systematic review of its emergence and applications of theory and methods. SSM – Population Health, page 100798, 2021.
  • Pananos, A. Demetri and Daniel J. Lizotte. Comparisons between Hamiltonian Monte Carlo and maximum a posteriori for a Bayesian model for apixaban induction dose & dose personalization. In Proceedings of the 5th Machine Learning for Healthcare Conference, volume 126 of Proceedings of Machine Learning Research, pages 397–417, Virtual, 07–08 Aug 2020. PMLR.
  • Jason E. Black, Jacqueline K. Kueper, Amanda L. Terry, and Daniel J. Lizotte. Development of a prognostic prediction model to estimate the risk of multiple chronic diseases: Constructing a copula-based model using Canadian primary care electronic medical record data. International Journal of Population Data Science, 6(1):1–18, January 2021.
  • Greta Bauer and Daniel J. Lizotte. Artificial intelligence, intersectionality, and the future of public health. American Journal of Public Health, 111(1), January 2021. Opinion Editorial (Peer reviewed).
  • Jason E. Black, Amanda L. Terry, and Daniel J. Lizotte. Development and evaluation of an osteoarthritis risk model for integration into primary care health information technology. International Journal of Medical Informatics, 141:104160, 2020.
  • Jacqueline Kueper, Amanda Terry, Merrick Zwarenstein, and Daniel J. Lizotte. Artificial Intelligence and primary care research: Ascoping review. The Annals of Family Medicine, 18:250–258, May 2020.
  • Davis, Brent D., Kamran Sedig, and Daniel J. Lizotte. Archetype-based modeling and search of social media. Big Data and Cognitive Computing, 3(3), 2019.
  • Daniel J. Lizotte and Arezoo Tahmasebi. Prediction and tolerance intervals for Dynamic Treatment Regimes. Statistical Methods in Medical Research, 26(4):1611–1629, 2017.
  • Maria Jahja and Daniel J. Lizotte. Visualizing clinical significance with prediction and tolerance regions. In Finale Doshi-Velez, Jim Fackler, David Kale, Rajesh Ranganath, Byron Wallace, and Jenna Wiens, editors, Proceedings of the 2nd Machine Learning for Healthcare Conference, volume 68 of Proceedings of Machine Learning Research, pages 217–230, Boston, Massachusetts, 18–19 Aug 2017. PMLR.
  • Daniel J. Lizotte and Eric B. Laber. Multi-objective Markov decision processes for data-driven decision support. Journal of Machine Learning Research, 17(211):1–28, 2016.
  • Rhiannon V. Rose and Daniel J. Lizotte. gLOP: the global and Local penalty for capturing predictive heterogeneity. In Proceedings of the 1st Machine Learning for Healthcare Conference, volume 56 of JMLR Workshop and Conference Proceedings, 2016. 8 pages.