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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
, David Page, Jennifer Cox, Elizabeth S. Burnside:
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
, Ming Yuan
, Jie Yin, Peggy L. Peissig, Alexander G. Cobian, David Page, Mark W. Craven:
Breast Cancer Risk Prediction Using Electronic Health Records. ICHI 2017: 224-228 - 2016
- [j13]Selen Bozkurt
, Francisco Gimenez, Elizabeth S. Burnside, Kemal Hakan Gülkesen
, Daniel L. Rubin
:
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
, Natasha K. Stout, Elizabeth S. Burnside:
Heterogeneity in Women's Adherence and Its Role in Optimal Breast Cancer Screening Policies. Manag. Sci. 62(5): 1339-1362 (2016) - [c36]Pedro Ferreira
, Inês Dutra
, Rogerio Salvini
, Elizabeth S. Burnside:
Interpretable models to predict Breast Cancer. BIBM 2016: 1507-1511 - [c35]Ricardo Sousa Rocha, Pedro Ferreira
, Inês de Castro Dutra
, Ricardo Cruz-Correia
, Rogerio Salvini
, Elizabeth S. Burnside:
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
, Peggy L. Peissig, Adedayo A. Onitilo, Jun Fan
, Ming Yuan
, Elizabeth S. Burnside:
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
, Nuno A. Fonseca
, Inês de Castro Dutra
, Ryan W. Woods, Elizabeth S. Burnside:
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
, Inês de Castro Dutra
, Ricardo Rocha
, Elizabeth S. Burnside:
SkILL - A Stochastic Inductive Logic Learner. ICMLA 2015: 555-558 - [p1]Marina Velikova, Inês Dutra
, Elizabeth S. Burnside:
Automated Diagnosis of Breast Cancer on Medical Images. Foundations of Biomedical Knowledge Representation 2015: 47-67 - 2014
- [j9]Hui Yang
, O. Erhun Kundakcioglu
, Jing Li, Teresa Wu, Joseph Ross Mitchell, Amy K. Hara, William Pavlicek, Leland S. Hu, Alvin C. Silva, Christine M. Zwart, Sait Tunç, Oguzhan Alagöz, Elizabeth S. Burnside, W. Art Chaovalitwongse, Georgiy Presnyakov, Yulian Cao, Sirirat Sujitnapitsatham, Daehan Won, Tara M. Madhyastha, Kurt E. Weaver, Paul R. Borghesani, Thomas J. Grabowski, Lianjie Shu, Man Ho Ling
, Shui Yee Wong, Kwok-Leung Tsui:
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
, Tiago T. V. Vinhoza
, Inês de Castro Dutra
, Paulo Vinicius Koerich Borges, Yirong Wu, Elizabeth S. Burnside:
Expert Bayes: Automatically Refining Manually Built Bayesian Networks. ICMLA 2014: 362-366 - [c26]Finn Kuusisto, Vítor Santos Costa
, Houssam Nassif, Elizabeth S. Burnside
, David Page, Jude W. Shavlik:
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
, Jingwei Li, Yirong Wu, Elizabeth S. Burnside:
ExpertBayes: Automatically refining manually built Bayesian networks. CoRR abs/1406.2395 (2014) - 2013
- [j8]Turgay Ayer
, Qiushi Chen
, Elizabeth S. Burnside
:
Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making. Comput. Math. Methods Medicine 2013: 832509:1-832509:10 (2013) - [j7]Oguzhan Alagöz
, Jagpreet Chhatwal, Elizabeth S. Burnside
:
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
, Mehmet Ayvaci
, Alejandro Munoz del Rio, David J. Vanness
, Ryan S. Woods, Elizabeth S. Burnside
:
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
, Houssam Nassif, Yirong Wu, Molly E. Klein, Heather B. Neuman, Jude W. Shavlik, Elizabeth S. Burnside
:
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
, David Page, Jude W. Shavlik, Vítor Santos Costa
:
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
, Daniel L. Rubin:
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
, Oguzhan Alagöz
, Elizabeth S. Burnside
:
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
, Ricardo Cruz-Correia
, Eliana Sousa, David Page, Elizabeth S. Burnside
, Inês de Castro Dutra
:
Extracting BI-RADS features from Portuguese clinical texts. BIBM 2012: 1-4 - [c17]Houssam Nassif, Vítor Santos Costa
, Elizabeth S. Burnside
, David Page:
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
, Nuno A. Fonseca
, Inês de Castro Dutra
, Ryan W. Woods, Elizabeth S. Burnside
:
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
, Elizabeth S. Burnside
:
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
, Gale A. Sisney, Lonie R. Salkowski, Josephine M. Harter, Bing Yu, Nirmala Ramanujam
:
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
, Mehmet Ayvaci
, Jude W. Shavlik, David Page:
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
, Elizabeth S. Burnside, Inês de Castro Dutra
, David Page, Vítor Santos Costa
:
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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