Year |
Citation |
Score |
2019 |
Gupta A, Slater JJ, Boyne D, Mitsakakis N, Béliveau A, Druzdzel MJ, Brenner DR, Hussain S, Arora P. Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method. Medical Decision Making : An International Journal of the Society For Medical Decision Making. 272989X19879095. PMID 31619130 DOI: 10.1177/0272989X19879095 |
0.362 |
|
2019 |
Arora P, Boyne D, Slater JJ, Gupta A, Brenner DR, Druzdzel MJ. Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine. Value in Health : the Journal of the International Society For Pharmacoeconomics and Outcomes Research. 22: 439-445. PMID 30975395 DOI: 10.1016/J.Jval.2019.01.006 |
0.416 |
|
2019 |
Orak NH, Small MJ, Druzdzel MJ. Bayesian network-based framework for exposure-response study design and interpretation. Environmental Health : a Global Access Science Source. 18: 23. PMID 30902096 DOI: 10.1186/S12940-019-0461-Y |
0.335 |
|
2019 |
Kraisangka J, Druzdzel MJ. Corrigendum to “A Bayesian network interpretation of the Cox's proportional hazard model” [Int. J. Approx. Reason. 103 (2018) 195–211] International Journal of Approximate Reasoning. 111: 51-52. DOI: 10.1016/J.Ijar.2019.04.011 |
0.353 |
|
2019 |
Kraisangka J, Lohmueller L, Kanwar M, Zhao C, Druzdzel M, Antaki J, Simon M, Benza R. Derivation of a Bayesian Network Model from an Existing Risk Score Calculator for Pulmonary Arterial Hypertension The Journal of Heart and Lung Transplantation. 38: S487-S488. DOI: 10.1016/J.Healun.2019.01.1240 |
0.306 |
|
2018 |
Kraisangka J, Druzdzel MJ. A Bayesian Network Interpretation of the Cox's Proportional Hazard Model. International Journal of Approximate Reasoning : Official Publication of the North American Fuzzy Information Processing Society. 103: 195-211. PMID 31130777 DOI: 10.1016/J.Ijar.2018.09.007 |
0.486 |
|
2018 |
Onisko A, Druzdzel MJ, Austin RM. Application of Bayesian network modeling to pathology informatics. Diagnostic Cytopathology. PMID 30451397 DOI: 10.1002/Dc.23993 |
0.421 |
|
2018 |
Arora P, Boyne D, Druzdzel M. Graphical Probabilistic Models for Risk Prediction and Decision Making Using Real-World Data: A Developing Tool for the Era of Precision Medicine Value in Health. 21: S10. DOI: 10.1016/J.Jval.2018.04.048 |
0.304 |
|
2018 |
BENZA R, KRAISANGKA J, LOHMUELLER L, ZHAO C, SELEJ M, DRUZDZEL M, ANTAKI J, SPECK J, KANWAR M. APPLICATION OF A BAYESIAN NETWORK MODEL TO PREDICT OUTCOMES IN PULMONARY ARTERIAL HYPERTENSION Chest. 154: 1061A. DOI: 10.1016/J.Chest.2018.08.961 |
0.312 |
|
2016 |
Zagorecki A, Łupińska-Dubicka A, Voortman M, Druzdzel MJ. Modeling Women's Menstrual Cycles using PICI Gates in Bayesian Network. International Journal of Approximate Reasoning : Official Publication of the North American Fuzzy Information Processing Society. 70: 123-136. PMID 26834313 DOI: 10.1016/J.Ijar.2015.12.002 |
0.722 |
|
2015 |
Ratnapinda P, Druzdzel MJ. Learning discrete Bayesian network parameters from continuous data streams: What is the best strategy? Journal of Applied Logic. DOI: 10.1016/J.Jal.2015.03.007 |
0.768 |
|
2014 |
Loghmanpour NA, Druzdzel MJ, Antaki JF. Cardiac Health Risk Stratification System (CHRiSS): a Bayesian-based decision support system for left ventricular assist device (LVAD) therapy. Plos One. 9: e111264. PMID 25397576 DOI: 10.1371/Journal.Pone.0111264 |
0.31 |
|
2014 |
de Jongh M, Druzdzel MJ. Evaluation of rules for coping with insufficient data in constraint-based search algorithms Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8754: 190-205. |
0.636 |
|
2014 |
Ratnapinda P, Druzdzel MJ. An empirical evaluation of costs and benefits of simplifying Bayesian networks by removing weak arcs Proceedings of the 27th International Florida Artificial Intelligence Research Society Conference, Flairs 2014. 508-511. |
0.729 |
|
2014 |
Kraisangka J, Druzdzel MJ, Druzdz MJ. Discrete Bayesian network interpretation of the Cox’s Proportional Hazards model Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8754: 238-253. |
0.312 |
|
2013 |
Oni?ko A, Druzdzel MJ. Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems. Artificial Intelligence in Medicine. 57: 197-206. PMID 23466438 DOI: 10.1016/J.Artmed.2013.01.004 |
0.453 |
|
2013 |
Ratnapinda P, Druzdzel MJ. An empirical comparison of Bayesian network parameter learning algorithms for continuous data streams Flairs 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference. 627-632. |
0.773 |
|
2011 |
Ratnapinda P, Druzdzel MJ. Does query-based diagnostics work? Ceur Workshop Proceedings. 818: 117-124. |
0.701 |
|
2010 |
Santelices LC, Wang Y, Severyn D, Druzdzel MJ, Kormos RL, Antaki JF. Development of a hybrid decision support model for optimal ventricular assist device weaning. The Annals of Thoracic Surgery. 90: 713-20. PMID 20732482 DOI: 10.1016/J.Athoracsur.2010.03.073 |
0.337 |
|
2010 |
Voortman M, Dash D, Druzdzel MJ. Learning why things change: The Difference-Based Causality Learner Proceedings of the 26th Conference On Uncertainty in Artificial Intelligence, Uai 2010. 641-650. |
0.748 |
|
2009 |
Ratnapinda P, Druzdzel MJ. Passive construction of diagnostic decision models: An empirical evaluation Proceedings of the International Multiconference On Computer Science and Information Technology, Imcsit '09. 4: 601-607. DOI: 10.1109/IMCSIT.2009.5352779 |
0.719 |
|
2009 |
Lu TC, Druzdzel MJ. Interactive construction of graphical decision models based on causal mechanisms European Journal of Operational Research. 199: 873-882. DOI: 10.1016/J.Ejor.2009.01.056 |
0.556 |
|
2008 |
Dash D, Druzdzel MJ. A note on the correctness of the causal ordering algorithm Artificial Intelligence. 172: 1800-1808. DOI: 10.1016/J.Artint.2008.06.005 |
0.56 |
|
2008 |
Voortman M, Druzdzel MJ. Insensitivity of constraint-based causal discovery algorithms to violations of the assumption of multivariate normality Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference, Flairs-21. 690-695. |
0.742 |
|
2007 |
De Jongh M, Druzdzel M, Rothkrantz L. Implementing and Improving a method for non-invasive elicitation of probabilities for bayesian networks Acm International Conference Proceeding Series. 285. DOI: 10.1145/1330598.1330722 |
0.622 |
|
2007 |
Yuan C, Druzdzel MJ. Theoretical analysis and practical insights on importance sampling in Bayesian networks International Journal of Approximate Reasoning. 46: 320-333. DOI: 10.1016/J.Ijar.2006.09.006 |
0.579 |
|
2006 |
Yuan C, Druzdzel MJ. Importance sampling algorithms for Bayesian networks: Principles and performance Mathematical and Computer Modelling. 43: 1189-1207. DOI: 10.1016/J.Mcm.2005.05.020 |
0.595 |
|
2006 |
Zagorecki A, Voortman M, Druzdzel MJ. Decomposing local probability distributions in bayesian networks for improved inference and parameter learning Flairs 2006 - Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference. 2006: 860-864. |
0.779 |
|
2002 |
Wang H, Dash D, Druzdzel MJ. A method for evaluating elicitation schemes for probabilistic models. Ieee Transactions On Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the Ieee Systems, Man, and Cybernetics Society. 32: 38-43. PMID 18238102 DOI: 10.1109/3477.979958 |
0.524 |
|
2001 |
Druzdzel MJ, Van Leijen H. Causal reversibility in Bayesian networks Journal of Experimental and Theoretical Artificial Intelligence. 13: 45-62. DOI: 10.1080/09528130120952 |
0.312 |
|
2001 |
Oniśko A, Druzdzel MJ, Wasyluk H. Learning Bayesian network parameters from small data sets: Application of Noisy-OR gates International Journal of Approximate Reasoning. 27: 165-182. DOI: 10.1016/S0888-613X(01)00039-1 |
0.45 |
|
2001 |
Dash D, Druzdzel M. Caveats for causal reasoning with equilibrium models Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 2143: 192-203. |
0.508 |
|
2000 |
Cheng J, Druzdzel MJ. AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks Journal of Artificial Intelligence Research. 13: 155-188. DOI: 10.1613/Jair.764 |
0.556 |
|
2000 |
Druzdzel M, van der Gaag L. Building probabilistic networks: "Where do the numbers come from?" guest editors' introduction Ieee Transactions On Knowledge and Data Engineering. 12: 481-486. DOI: 10.1109/Tkde.2000.868901 |
0.371 |
|
1999 |
Lin Y, Druzdzel MJ. Relevance-based incremental belief updating in Bayesian networks International Journal of Pattern Recognition and Artificial Intelligence. 13: 285-295. DOI: 10.1142/S0218001499000161 |
0.403 |
|
Show low-probability matches. |