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Elizabeth S. Burnside
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- affiliation: University of Wisconsin, USA
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2010 – 2019
- 2018
- [j14]Hakan Bulu, Dorothy A. Sippo, Janie M. Lee, Elizabeth S. Burnside, Daniel L. Rubin:
Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports. J. Digit. Imaging 31(5): 596-603 (2018) - [c40]Shara Feld, Kaitlin M. Woo, Roxana Alexandridis, Yirong Wu, Jie Liu, Peggy L. Peissig, Adedayo A. Onitilo, Jennifer Cox, David Page, Elizabeth S. Burnside:
Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants. AMIA 2018 - [c39]Lonie R. Salkowski, Mai Elezaby, Amy M. Fowler, Elizabeth S. Burnside, Ryan W. Woods, Roberta M. Strigel:
Comparison of screening full-field digital mammography and digital breast tomosynthesis technical recalls. IWBI 2018: 1071820 - [c38]Yirong Wu, Jun Fan
, Peggy L. Peissig, Richard L. Berg, Ahmad Pahlavan Tafti, Jie Yin, Ming Yuan
Quantifying predictive capability of electronic health records for the most harmful breast cancer. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2018: 105770J - 2017
- [c37]Yirong Wu, Elizabeth S. Burnside, Jennifer Cox, Jun Fan
Breast Cancer Risk Prediction Using Electronic Health Records. ICHI 2017: 224-228 - 2016
- [j13]Selen Bozkurt
Using automatically extracted information from mammography reports for decision-support. J. Biomed. Informatics 62: 224-231 (2016) - [j12]Jun Fan, Yirong Wu, Ming Yuan, David Page, Jie Liu, Irene M. Ong, Peggy L. Peissig, Elizabeth S. Burnside:
Structure-Leveraged Methods in Breast Cancer Risk Prediction. J. Mach. Learn. Res. 17: 85:1-85:15 (2016) - [j11]Turgay Ayer, Oguzhan Alagöz
Heterogeneity in Women's Adherence and Its Role in Optimal Breast Cancer Screening Policies. Manag. Sci. 62(5): 1339-1362 (2016) - [c36]Pedro Ferreira
Interpretable models to predict Breast Cancer. BIBM 2016: 1507-1511 - [c35]Ricardo Sousa Rocha, Pedro Ferreira
A Speech-to-Text Interface for MammoClass. CBMS 2016: 1-6 - [c34]Craig K. Abbey, Yirong Wu, Elizabeth S. Burnside, Adam Wunderlich, Frank W. Samuelson, John M. Boone:
A utility/cost analysis of breast cancer risk prediction algorithms. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2016: 97871J - [c33]Yirong Wu, Craig K. Abbey, Jie Liu, Irene M. Ong
Discriminatory power of common genetic variants in personalized breast cancer diagnosis. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2016: 978706 - 2015
- [j10]Pedro Ferreira
Predicting malignancy from mammography findings and image-guided core biopsies. Int. J. Data Min. Bioinform. 11(3): 257-276 (2015) - [c32]Joana Côrte-Real, Theofrastos Mantadelis
SkILL - A Stochastic Inductive Logic Learner. ICMLA 2015: 555-558 - [p1]Marina Velikova, Inês Dutra
Automated Diagnosis of Breast Cancer on Medical Images. Foundations of Biomedical Knowledge Representation 2015: 47-67 - 2014
- [j9]Hui Yang
Healthcare Intelligence: Turning Data into Knowledge. IEEE Intell. Syst. 29(3): 54-68 (2014) - [c31]Jie Liu, Chunming Zhang, Elizabeth S. Burnside, David Page:
Learning Heterogeneous Hidden Markov Random Fields. AISTATS 2014: 576-584 - [c30]Francisco Gimenez, Yirong Wu, Elizabeth S. Burnside, Daniel L. Rubin:
A Novel Method to Assess Incompleteness of Mammography Report Content. AMIA 2014 - [c29]Yirong Wu, Jie Liu, David Page, Peggy L. Peissig, Catherine A. McCarty, Adedayo A. Onitilo, Elizabeth S. Burnside:
Comparing the Value of Mammographic Features and Genetic Variants in Breast Cancer Risk Prediction. AMIA 2014 - [c28]Jie Liu, Chunming Zhang, Elizabeth S. Burnside, David Page:
Multiple Testing under Dependence via Semiparametric Graphical Models. ICML 2014: 955-963 - [c27]Ezilda Almeida, Pedro Ferreira
Expert Bayes: Automatically Refining Manually Built Bayesian Networks. ICMLA 2014: 362-366 - [c26]Finn Kuusisto, Vítor Santos Costa
Support Vector Machines for Differential Prediction. ECML/PKDD (2) 2014: 50-65 - [i2]Ezilda Almeida, Pedro Ferreira, Tiago T. V. Vinhoza, Inês de Castro Dutra
ExpertBayes: Automatically refining manually built Bayesian networks. CoRR abs/1406.2395 (2014) - 2013
- [j8]Turgay Ayer
Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making. Comput. Math. Methods Medicine 2013: 832509:1-832509:10 (2013) - [j7]Oguzhan Alagöz
Optimal Policies for Reducing Unnecessary Follow-Up Mammography Exams in Breast Cancer Diagnosis. Decis. Anal. 10(3): 200-224 (2013) - [j6]Yirong Wu, Oguzhan Alagöz
A Comprehensive Methodology for Determining the Most Informative Mammographic Features. J. Digit. Imaging 26(5): 941-947 (2013) - [c25]Jie Liu, David Page, Houssam Nassif, Jude W. Shavlik, Peggy L. Peissig, Catherine A. McCarty, Adedayo A. Onitilo, Elizabeth S. Burnside:
Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms. AMIA 2013 - [c24]Yirong Wu, David J. Vanness, Elizabeth S. Burnside:
Using Multidimensional Mutual Information to Prioritize Mammographic Features for Breast Cancer Diagnosis. AMIA 2013 - [c23]Finn Kuusisto, Inês de Castro Dutra
Using machine learning to identify benign cases with non-definitive biopsy. Healthcom 2013: 283-285 - [c22]Houssam Nassif, Finn Kuusisto, Elizabeth S. Burnside, Jude W. Shavlik:
Uplift Modeling with ROC: An SRL Case Study. ILP (Late Breaking Papers) 2013: 40-45 - [c21]Houssam Nassif, Finn Kuusisto, Elizabeth S. Burnside
Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling. ECML/PKDD (3) 2013: 595-611 - 2012
- [j5]Bethany Percha, Houssam Nassif, Jafi A. Lipson, Elizabeth S. Burnside
Automatic classification of mammography reports by BI-RADS breast tissue composition class. J. Am. Medical Informatics Assoc. 19(5): 913-916 (2012) - [j4]Mehmet Ayvaci
The Effect of Budgetary Restrictions on Breast Cancer Diagnostic Decisions. Manuf. Serv. Oper. Manag. 14(4): 600-617 (2012) - [c20]Houssam Nassif, Yirong Wu, David Page, Elizabeth S. Burnside:
Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women. AMIA 2012 - [c19]Jie Liu, Elizabeth S. Burnside, Humberto J. Vidaillet, David Page:
A collective ranking method for genome-wide association studies. BCB 2012: 313-320 - [c18]Houssam Nassif, Filipe Cunha, Inês C. Moreira
Extracting BI-RADS features from Portuguese clinical texts. BIBM 2012: 1-4 - [c17]Houssam Nassif, Vítor Santos Costa
Relational Differential Prediction. ECML/PKDD (1) 2012: 617-632 - [c16]Jie Liu, Chunming Zhang, Catherine A. McCarty, Peggy L. Peissig, Elizabeth S. Burnside, David Page:
Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies. UAI 2012: 511-522 - [c15]Jie Liu, Chunming Zhang, Catherine A. McCarty, Peggy L. Peissig, Elizabeth S. Burnside, David Page:
High-Dimensional Structured Feature Screening Using Binary Markov Random Fields. AISTATS 2012: 712-721 - [i1]Jie Liu, Chunming Zhang, Catherine A. McCarty, Peggy L. Peissig, Elizabeth S. Burnside, David Page:
Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies. CoRR abs/1210.4868 (2012) - 2011
- [c14]Pedro Ferreira
Predicting Malignancy from Mammography Findings and Surgical Biopsies. BIBM 2011: 339-344 - [c13]Pedro Ferreira, Inês de Castro Dutra, Nuno A. Fonseca, Ryan W. Woods, Elizabeth S. Burnside:
Studying the Relevance of Breast Imaging Features. HEALTHINF 2011: 337-342 - 2010
- [j3]Jagpreet Chhatwal, Oguzhan Alagöz
Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors. Oper. Res. 58(6): 1577-1591 (2010) - [j2]Ryan W. Woods, Louis Oliphant, Kazuhiko Shinki, David Page, Jude W. Shavlik, Elizabeth S. Burnside
Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer. J. Digit. Imaging 23(5): 554-561 (2010) - [c12]Houssam Nassif, David Page, Mehmet Ayvaci, Jude W. Shavlik, Elizabeth S. Burnside:
Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming. IHI 2010: 76-82
2000 – 2009
- 2009
- [j1]Changfang Zhu, Elizabeth S. Burnside
Fluorescence Spectroscopy: An Adjunct Diagnostic Tool to Image-Guided Core Needle Biopsy of the Breast. IEEE Trans. Biomed. Eng. 56(10): 2518-2528 (2009) - [c11]Houssam Nassif, Ryan W. Woods, Elizabeth S. Burnside
Information Extraction for Clinical Data Mining: A Mammography Case Study. ICDM Workshops 2009: 37-42 - [c10]Louis Oliphant, Elizabeth S. Burnside, Jude W. Shavlik:
Boosting First-Order Clauses for Large, Skewed Data Sets. ILP 2009: 166-177 - 2007
- [c9]Jesse Davis, Irene M. Ong, Jan Struyf, Elizabeth S. Burnside, David Page, Vítor Santos Costa:
Change of Representation for Statistical Relational Learning. IJCAI 2007: 2719-2726 - 2005
- [c8]Elizabeth S. Burnside, Jesse Davis, Vítor Santos Costa, Inês de Castro Dutra, Charles E. Kahn Jr., Jason Fine, David Page:
Knowledge Discovery from Structured Mammography Reports Using Inductive Logic Programming. AMIA 2005 - [c7]Jesse Davis
An Integrated Approach to Learning Bayesian Networks of Rules. ECML 2005: 84-95 - [c6]Jesse Davis, Elizabeth S. Burnside, Inês de Castro Dutra, David Page, Raghu Ramakrishnan, Vítor Santos Costa, Jude W. Shavlik:
View Learning for Statistical Relational Learning: With an Application to Mammography. IJCAI 2005: 677-683 - 2004
- [c5]Elizabeth S. Burnside, Daniel L. Rubin, Ross D. Shachter:
Improving a Bayesian network's ability to predict the probability of malignancy of microcalcifications on mammography. CARS 2004: 1021-1026 - [c4]Yue Pan, Elizabeth S. Burnside:
The effects of training parameters on learning a probabilistic expert system for mammography. CARS 2004: 1027-1032 - [c3]Elizabeth S. Burnside, Daniel L. Rubin, Ross D. Shachter:
Using a Bayesian Network to Predict the Probability and Type of Breast Cancer Represented by Microcalcifications on Mammography. MedInfo 2004: 13-17 - 2000
- [c2]Elizabeth S. Burnside, Daniel L. Rubin, Ross D. Shachter:
A Bayesian network for mammography. AMIA 2000
1990 – 1999
- 1999
- [c1]Jin S. Hahn, Elizabeth S. Burnside, James F. Brinkley, Cornelius Rosse, Mark A. Musen:
Representing the Digital Anatomist Foundational Model as a Protege Ontology. AMIA 1999
Coauthor Index
aka: Inês Dutra
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