Elizabeth S. Burnside

Publication Activity (10 Years)

Years Active: 1999-2018
Publications (10 Years): 12

Top Topics

Relational Learning

Electronic Health Records

Top Venues

Medical Imaging: Image Perception, Observer Performance, and Technology Assessment

Publications

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)
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)
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)
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) (2018)
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)
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 (2016)
Pedro Ferreira, Inês Dutra, Rogerio Salvini, Elizabeth S. Burnside
Interpretable models to predict Breast Cancer. BIBM (2016)
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 (2016)
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)
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)
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) (2016)
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)
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) (2015)
Joana Côrte-Real, Theofrastos Mantadelis, Inês de Castro Dutra, Ricardo Rocha, Elizabeth S. Burnside
SkILL - A Stochastic Inductive Logic Learner. ICMLA (2015)
Jie Liu, Chunming Zhang, Elizabeth S. Burnside, David Page
Multiple Testing under Dependence via Semiparametric Graphical Models. ICML (2014)
Jie Liu, Chunming Zhang, Elizabeth S. Burnside, David Page
Learning Heterogeneous Hidden Markov Random Fields. AISTATS (2014)
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)
Francisco Gimenez, Yirong Wu, Elizabeth S. Burnside, Daniel L. Rubin
A Novel Method to Assess Incompleteness of Mammography Report Content. AMIA (2014)
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)
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)
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 (2014)
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 Tunc, 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) (2014)
Houssam Nassif, Finn Kuusisto, Elizabeth S. Burnside, Jude W. Shavlik
Uplift Modeling with ROC: An SRL Case Study. ILP (Late Breaking Papers) (2013)
Oguzhan Alagöz, Jagpreet Chhatwal, Elizabeth S. Burnside
Optimal Policies for Reducing Unnecessary Follow-Up Mammography Exams in Breast Cancer Diagnosis. Decis. Anal. 10 (3) (2013)
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)
Yirong Wu, David J. Vanness, Elizabeth S. Burnside
Using Multidimensional Mutual Information to Prioritize Mammographic Features for Breast Cancer Diagnosis. AMIA (2013)
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)
Turgay Ayer, Qiushi Chen, Elizabeth S. Burnside
Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making. Comput. Math. Methods Medicine 2013 (2013)
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) (2013)
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)
Mehmet Ayvaci, Oguzhan Alagöz, Elizabeth S. Burnside
The Effect of Budgetary Restrictions on Breast Cancer Diagnostic Decisions. Manuf. Serv. Oper. Manag. 14 (4) (2012)
Jie Liu, Elizabeth S. Burnside, Humberto J. Vidaillet, David Page
A collective ranking method for genome-wide association studies. BCB (2012)
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 (2012)
Houssam Nassif, Yirong Wu, David Page, Elizabeth S. Burnside
Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women. AMIA (2012)
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) (2012)
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)
Houssam Nassif, Vítor Santos Costa, Elizabeth S. Burnside, David Page
Relational Differential Prediction. ECML/PKDD (1) (2012)
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)
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)
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)
Pedro Ferreira, Inês de Castro Dutra, Nuno A. Fonseca, Ryan W. Woods, Elizabeth S. Burnside
Studying the Relevance of Breast Imaging Features. HEALTHINF (2011)
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) (2010)
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)
Jagpreet Chhatwal, Oguzhan Alagöz, Elizabeth S. Burnside
Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors. Oper. Res. 58 (6) (2010)
Louis Oliphant, Elizabeth S. Burnside, Jude W. Shavlik
Boosting First-Order Clauses for Large, Skewed Data Sets. ILP (2009)
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) (2009)
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)
Jesse Davis, Irene M. Ong, Jan Struyf, Elizabeth S. Burnside, David Page, Vítor Santos Costa
Change of Representation for Statistical Relational Learning. IJCAI (2007)
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)
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)
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)
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)
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)
Yue Pan, Elizabeth S. Burnside
The effects of training parameters on learning a probabilistic expert system for mammography. CARS (2004)
Elizabeth S. Burnside, Daniel L. Rubin, Ross D. Shachter
A Bayesian network for mammography. AMIA (2000)
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)