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ORCIDDaniel J. Lizotte
Person information
- affiliation: University of Western Ontario, ON, Canada
- affiliation (Ph.D.): University of Alberta, Edmonton, Canada
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Journal Articles
- 2023
[j15]Cheryl Forchuk, Abraham Rudnick
, Deborah Corring, Daniel J. Lizotte, Jeffrey S. Hoch, Richard Booth, Barbara Frampton, Rupinder Mann, Jonathan Serrato:
A Smart Technology Intervention in the Homes of People with Mental Illness and Physical Comorbidities. Sensors 23(1): 406 (2023)- 2022
- [j14]Amanda L. Terry, Jacqueline K. Kueper, Ron Beleno, Judith Belle Brown, Sonny Cejic, Janet Dang, Daniel Leger, Scott McKay, Leslie Meredith, Andrew D. Pinto, Bridget L. Ryan, Moira Stewart, Merrick Zwarenstein, Daniel J. Lizotte:
Is primary health care ready for artificial intelligence? What do primary health care stakeholders say? BMC Medical Informatics Decis. Mak. 22(1): 237 (2022) - 2021
- [j13]Maede Nouri, Daniel J. Lizotte, Kamran Sedig, Sheikh S. Abdullah:
VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data. Data 6(8): 85 (2021) - [j12]Moutasem A. Zakkar, Daniel J. Lizotte:
Analyzing Patient Stories on Social Media Using Text Analytics. J. Heal. Informatics Res. 5(4): 382-400 (2021) - 2020
- [j11]Jason E. Black, Amanda L. Terry, Daniel J. Lizotte:
Development and evaluation of an osteoarthritis risk model for integration into primary care health information technology. Int. J. Medical Informatics 141: 104160 (2020) - [j10]Sheikh S. Abdullah, Neda Rostamzadeh, Kamran Sedig, Daniel J. Lizotte, Amit X. Garg, Eric McArthur:
Machine Learning for Identifying Medication-Associated Acute Kidney Injury. Informatics 7(2): 18 (2020) - 2019
- [j9]Brent D. Davis, Kamran Sedig, Daniel J. Lizotte:
Archetype-Based Modeling and Search of Social Media. Big Data Cogn. Comput. 3(3): 44 (2019) - [j8]Emma Farago, Shrikant Chinchalkar, Daniel J. Lizotte, Ana Luisa Trejos:
Development of an EMG-Based Muscle Health Model for Elbow Trauma Patients. Sensors 19(15): 3309 (2019) - 2016
- [j7]Daniel J. Lizotte, Eric B. Laber:
Multi-Objective Markov Decision Processes for Data-Driven Decision Support. J. Mach. Learn. Res. 17: 211:1-211:28 (2016) - 2014
- [j6]George Zhu, Daniel J. Lizotte, Jesse Hoey:
Scalable approximate policies for Markov decision process models of hospital elective admissions. Artif. Intell. Medicine 61(1): 21-34 (2014) - [j5]Luiza Antonie, Kris Inwood, Daniel J. Lizotte, J. Andrew Ross:
Tracking people over time in 19th century Canada for longitudinal analysis. Mach. Learn. 95(1): 129-146 (2014) - 2012
- [j4]Daniel J. Lizotte, Russell Greiner, Dale Schuurmans:
An experimental methodology for response surface optimization methods. J. Glob. Optim. 53(4): 699-736 (2012) - [j3]Daniel J. Lizotte, Michael Bowling, Susan A. Murphy:
Linear fitted-Q iteration with multiple reward functions. J. Mach. Learn. Res. 13: 3253-3295 (2012) - 2011
- [j2]Susan M. Shortreed, Eric B. Laber, Daniel J. Lizotte, T. Scott Stroup, Joelle Pineau, Susan A. Murphy:
Informing sequential clinical decision-making through reinforcement learning: an empirical study. Mach. Learn. 84(1-2): 109-136 (2011) - 2001
- [j1]Ruth E. Shaw, Lawrence E. Garey, Daniel J. Lizotte:
A parallel numerical algorithm for fredholm integro-differential two-point boundary value problems. Int. J. Comput. Math. 77(2): 305-318 (2001)
Conference and Workshop Papers
- 2022
- [c26]Cheryl Forchuk, Abraham Rudnick, Deborah Corring, Daniel J. Lizotte, Jeffrey S. Hoch, Richard Booth, Barbara Frampton, Rupinder Mann, Jonathan Serrato:
Smart Technology in the Home for People Living in the Community with Mental Illness and Physical Comorbidities. ICOST 2022: 86-99 - 2021
- [c25]Mozhgan Salimiparsa, Daniel J. Lizotte, Kamran Sedig:
A User-Centered Design of Explainable AI for Clinical Decision Support. Canadian AI 2021 - [c24]Caroline Strickland, Daniel J. Lizotte:
Hierarchical Reinforcement Learning for Decision Support in Health Care. Canadian AI 2021 - [c23]Chris Brogly, Daniel J. Lizotte, Michael A. Bauer:
Ecological Momentary Assessment extensions 3 (EMAX3) Proposal: An app for EMA-type research. ISCC 2021: 1-4 - 2020
- [c22]Cheryl Forchuk, Sandra Fisman, Jeffrey P. Reiss, Kerry Collins, Julie Eichstedt, Abraham Rudnick, Wanrudee Isaranuwatchai, Jeffrey S. Hoch, Xianbin Wang, Daniel J. Lizotte, Shona Macpherson, Richard Booth:
Improving Access and Mental Health for Youth Through Virtual Models of Care. ICOST 2020: 210-220 - [c21]Athanasios Demetri Pananos, Daniel J. Lizotte:
Comparisons Between Hamiltonian Monte Carlo and Maximum A Posteriori For A Bayesian Model For Apixaban Induction Dose & Dose Personalization. MLHC 2020: 397-417 - 2017
- [c20]Maria Jahja, Daniel J. Lizotte:
Visualizing Clinical Significance with Prediction and Tolerance Regions. MLHC 2017: 217-230 - 2016
- [c19]Rhiannon V. Rose, Daniel J. Lizotte:
gLOP: the global and Local Penalty for Capturing Predictive Heterogeneity. MLHC 2016: 134-149 - 2015
- [c18]Michael Cormier, Daniel J. Lizotte, Richard Mann:
Reconstruction of 3-D Density Functions from Few Projections: Structural Assumptions for Graceful Degradation. CRV 2015: 147-154 - 2013
- [c17]Daniel J. Lizotte, Michael Bowling, Susan A. Murphy:
Linear Fitted-Q Iteration with Multiple Reward Functions. ICAPS 2013 - [c16]Adedamola Adepetu, Elnaz Rezaei, Daniel J. Lizotte, Srinivasan Keshav:
Critiquing Time-of-Use pricing in Ontario. SmartGridComm 2013: 223-228 - [c15]Tameem Adel, Benn Smith, Ruth Urner, Daniel W. Stashuk, Daniel J. Lizotte:
Generative Multiple-Instance Learning Models For Quantitative Electromyography. UAI 2013 - 2012
- [c14]Shehroz S. Khan, Jesse Hoey, Daniel J. Lizotte:
Bayesian Multiple Imputation Approaches for One-Class Classification. Canadian AI 2012: 331-336 - [c13]Atif Khan, John A. Doucette, Robin Cohen, Daniel J. Lizotte:
Integrating Machine Learning Into a Medical Decision Support System to Address the Problem of Missing Patient Data. ICMLA (1) 2012: 454-457 - [c12]Rayman Preet Singh, Peter Xiang Gao, Daniel J. Lizotte:
On hourly home peak load prediction. SmartGridComm 2012: 163-168 - 2011
- [c11]Daniel J. Lizotte:
Convergent Fitted Value Iteration with Linear Function Approximation. NIPS 2011: 2537-2545 - 2010
- [c10]Daniel J. Lizotte, Michael H. Bowling, Susan A. Murphy:
Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis. ICML 2010: 695-702 - 2007
- [c9]Daniel J. Lizotte, Tao Wang, Michael H. Bowling, Dale Schuurmans:
Automatic Gait Optimization with Gaussian Process Regression. IJCAI 2007: 944-949 - [c8]Tao Wang, Daniel J. Lizotte, Michael H. Bowling, Dale Schuurmans:
Stable Dual Dynamic Programming. NIPS 2007: 1569-1576 - 2006
- [c7]Qin Iris Wang, Colin Cherry, Daniel J. Lizotte, Dale Schuurmans:
Improved Large Margin Dependency Parsing via Local Constraints and Laplacian Regularization. CoNLL 2006: 21-28 - 2005
- [c6]Tao Wang, Daniel J. Lizotte, Michael H. Bowling, Dale Schuurmans:
Bayesian sparse sampling for on-line reward optimization. ICML 2005: 956-963 - 2004
- [c5]Omid Madani, Daniel J. Lizotte, Russell Greiner:
The Budgeted Multi-armed Bandit Problem. COLT 2004: 643-645 - [c4]Daniel J. Lizotte, Hong Zhang:
Trading confidence for communications. SMC (1) 2004: 935-940 - [c3]Omid Madani, Daniel J. Lizotte, Russell Greiner:
Active Model Selection. UAI 2004: 357-365 - 2003
- [c2]Daniel J. Lizotte, Omid Madani, Russell Greiner:
Budgeted Learning of Naive-Bayes Classifiers. UAI 2003: 378-385 - 2002
- [c1]Ruth E. Shaw, Lawrence E. Garey, Daniel J. Lizotte:
A Parallel Numerical Algorithm for Boundary-Value FIDES on a PC Cluster. IPDPS 2002
Informal and Other Publications
- 2023
- [i11]Caroline Strickland, Chandrika Saha, Muhammad Zakar, Sareh Nejad, Noshin Tasnim, Daniel J. Lizotte, Anwar Haque:
DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection. CoRR abs/2301.03368 (2023) - [i10]Nathan Phelps, Stephanie Marrocco, Stephanie Cornell, Dalton L. Wolfe, Daniel J. Lizotte:
Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation. CoRR abs/2310.14976 (2023) - [i9]Rouzbeh Meshkinnejad, Jie Mei, Daniel J. Lizotte, Yalda Mohsenzadeh:
Look-Ahead Selective Plasticity for Continual Learning of Visual Tasks. CoRR abs/2311.01617 (2023) - 2022
- [i8]Jacqueline K. Kueper, Jennifer Rayner, Daniel J. Lizotte:
Hybrid Feature- and Similarity-Based Models for Prediction and Interpretation using Large-Scale Observational Data. CoRR abs/2204.06076 (2022) - 2019
- [i7]Brent D. Davis, Ethan Jackson, Daniel J. Lizotte:
Decision-Directed Data Decomposition. CoRR abs/1909.08159 (2019) - 2016
- [i6]Rhiannon V. Rose, Daniel J. Lizotte:
gLOP: the global and Local Penalty for Capturing Predictive Heterogeneity. CoRR abs/1608.00027 (2016) - 2014
- [i5]Robert Suderman, Daniel J. Lizotte, Nasser Mohieddin Abukhdeir:
Theory and Application of Shapelets to the Analysis of Surface Self-assembly Imaging. CoRR abs/1404.0437 (2014) - 2013
- [i4]Tameem Adel, Benn Smith, Ruth Urner, Daniel W. Stashuk, Daniel J. Lizotte:
Generative Multiple-Instance Learning Models For Quantitative Electromyography. CoRR abs/1309.6811 (2013) - 2012
- [i3]Eric B. Laber, Daniel J. Lizotte, Bradley Ferguson:
Set-valued dynamic treatment regimes for competing outcomes. CoRR abs/1207.3100 (2012) - [i2]Omid Madani, Daniel J. Lizotte, Russell Greiner:
Active Model Selection. CoRR abs/1207.4138 (2012) - [i1]Daniel J. Lizotte, Omid Madani, Russell Greiner:
Budgeted Learning of Naive-Bayes Classifiers. CoRR abs/1212.2472 (2012)
Coauthor Index
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