Elizabeth S. Burnside

57 papers A* 3A 3B 2C 3Misc 9Journal 16Unranked 20

Year	Rank	Type	Title / Venue / Authors
2018	—	`conf`	Comparison of screening full-field digital mammography and digital breast tomosynthesis technical recalls. IWBI Lonie R. Salkowski, Mai Elezaby, Amy M. Fowler, Elizabeth S. Burnside, Ryan W. Woods, Roberta M. Strigel
2018	Misc	`conf`	Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants. AMIA Shara Feld, Kaitlin M. Woo, Roxana Alexandridis, Yirong Wu, Jie Liu, Peggy L. Peissig, Adedayo A. Onitilo, Jennifer Cox, David Page, Elizabeth S. Burnside
2018	J	`jnl`	Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports. J. Digit. Imaging Hakan Bulu, Dorothy A. Sippo, Janie M. Lee, Elizabeth S. Burnside, Daniel L. Rubin
2018	—	`conf`	Quantifying predictive capability of electronic health records for the most harmful breast cancer. Image Perception, Observer Performance, and Technology Assessment Yirong Wu, Jun Fan, Peggy L. Peissig, Richard L. Berg, Ahmad Pahlavan Tafti, Jie Yin, Ming Yuan, David Page, Jennifer Cox, Elizabeth S. Burnside
2017	—	`conf`	Breast Cancer Risk Prediction Using Electronic Health Records. ICHI Yirong Wu, Elizabeth S. Burnside, Jennifer Cox, Jun Fan, Ming Yuan, Jie Yin, Peggy L. Peissig, Alexander G. Cobian, David Page, Mark W. Craven
2016	B	`conf`	A Speech-to-Text Interface for MammoClass. CBMS Ricardo Sousa Rocha, Pedro Ferreira, Inês de Castro Dutra, Ricardo Cruz-Correia, Rogerio Salvini, Elizabeth S. Burnside
2016	—	`conf`	A utility/cost analysis of breast cancer risk prediction algorithms. Image Perception, Observer Performance, and Technology Assessment Craig K. Abbey, Yirong Wu, Elizabeth S. Burnside, Adam Wunderlich, Frank W. Samuelson, John M. Boone
2016	—	`conf`	Discriminatory power of common genetic variants in personalized breast cancer diagnosis. Image Perception, Observer Performance, and Technology Assessment Yirong Wu, Craig K. Abbey, Jie Liu, Irene M. Ong, Peggy L. Peissig, Adedayo A. Onitilo, Jun Fan, Ming Yuan, Elizabeth S. Burnside
2016	J	`jnl`	Heterogeneity in Women's Adherence and Its Role in Optimal Breast Cancer Screening Policies. Manag. Sci. Turgay Ayer, Oguzhan Alagöz, Natasha K. Stout, Elizabeth S. Burnside
2016	—	`conf`	Interpretable models to predict Breast Cancer. BIBM Pedro Ferreira, Inês Dutra, Rogerio Salvini, Elizabeth S. Burnside
2016	J	`jnl`	Structure-Leveraged Methods in Breast Cancer Risk Prediction. J. Mach. Learn. Res. Jun Fan, Yirong Wu, Ming Yuan, David Page, Jie Liu, Irene M. Ong, Peggy L. Peissig, Elizabeth S. Burnside
2016	J	`jnl`	Using automatically extracted information from mammography reports for decision-support. J. Biomed. Informatics Selen Bozkurt, Francisco Gimenez, Elizabeth S. Burnside, Kemal Hakan Gülkesen, Daniel L. Rubin
2015	—	`ch.`	Automated Diagnosis of Breast Cancer on Medical Images. Foundations of Biomedical Knowledge Representation Marina Velikova, Inês Dutra, Elizabeth S. Burnside
2015	J	`jnl`	Predicting malignancy from mammography findings and image-guided core biopsies. Int. J. Data Min. Bioinform. Pedro Ferreira, Nuno A. Fonseca, Inês de Castro Dutra, Ryan W. Woods, Elizabeth S. Burnside
2015	C	`conf`	SkILL - A Stochastic Inductive Logic Learner. ICMLA Joana Côrte-Real, Theofrastos Mantadelis, Inês de Castro Dutra, Ricardo Rocha, Elizabeth S. Burnside
2014	Misc	`conf`	A Novel Method to Assess Incompleteness of Mammography Report Content. AMIA Francisco Gimenez, Yirong Wu, Elizabeth S. Burnside, Daniel L. Rubin
2014	Misc	`conf`	Comparing the Value of Mammographic Features and Genetic Variants in Breast Cancer Risk Prediction. AMIA Yirong Wu, Jie Liu, David Page, Peggy L. Peissig, Catherine A. McCarty, Adedayo A. Onitilo, Elizabeth S. Burnside
2014	C	`conf`	Expert Bayes: Automatically Refining Manually Built Bayesian Networks. ICMLA Ezilda Almeida, Pedro Ferreira, Tiago T. V. Vinhoza, Inês de Castro Dutra, Paulo Vinicius Koerich Borges, Yirong Wu, Elizabeth S. Burnside
2014	J	`jnl`	ExpertBayes: Automatically refining manually built Bayesian networks. CoRR Ezilda Almeida, Pedro Ferreira, Tiago T. V. Vinhoza, Inês de Castro Dutra, Jingwei Li, Yirong Wu, Elizabeth S. Burnside
2014	J	`jnl`	Healthcare Intelligence: Turning Data into Knowledge. IEEE Intell. Syst. 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
2014	A	`conf`	Learning Heterogeneous Hidden Markov Random Fields. AISTATS Jie Liu, Chunming Zhang, Elizabeth S. Burnside, David Page
2014	A*	`conf`	Multiple Testing under Dependence via Semiparametric Graphical Models. ICML Jie Liu, Chunming Zhang, Elizabeth S. Burnside, David Page
2014	—	`conf`	Support Vector Machines for Differential Prediction. ECML/PKDD (2) Finn Kuusisto, Vítor Santos Costa, Houssam Nassif, Elizabeth S. Burnside, David Page, Jude W. Shavlik
2013	J	`jnl`	A Comprehensive Methodology for Determining the Most Informative Mammographic Features. J. Digit. Imaging Yirong Wu, Oguzhan Alagöz, Mehmet Ayvaci, Alejandro Munoz del Rio, David J. Vanness, Ryan S. Woods, Elizabeth S. Burnside
2013	J	`jnl`	Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making. Comput. Math. Methods Medicine Turgay Ayer, Qiushi Chen, Elizabeth S. Burnside
2013	Misc	`conf`	Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms. AMIA Jie Liu, David Page, Houssam Nassif, Jude W. Shavlik, Peggy L. Peissig, Catherine A. McCarty, Adedayo A. Onitilo, Elizabeth S. Burnside
2013	J	`jnl`	Optimal Policies for Reducing Unnecessary Follow-Up Mammography Exams in Breast Cancer Diagnosis. Decis. Anal. Oguzhan Alagöz, Jagpreet Chhatwal, Elizabeth S. Burnside
2013	—	`conf`	Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling. ECML/PKDD (3) Houssam Nassif, Finn Kuusisto, Elizabeth S. Burnside, David Page, Jude W. Shavlik, Vítor Santos Costa
2013	—	`conf`	Uplift Modeling with ROC: An SRL Case Study. ILP (Late Breaking Papers) Houssam Nassif, Finn Kuusisto, Elizabeth S. Burnside, Jude W. Shavlik
2013	Misc	`conf`	Using Multidimensional Mutual Information to Prioritize Mammographic Features for Breast Cancer Diagnosis. AMIA Yirong Wu, David J. Vanness, Elizabeth S. Burnside
2013	C	`conf`	Using machine learning to identify benign cases with non-definitive biopsy. Healthcom Finn Kuusisto, Inês de Castro Dutra, Houssam Nassif, Yirong Wu, Molly E. Klein, Heather B. Neuman, Jude W. Shavlik, Elizabeth S. Burnside
2012	—	`conf`	A collective ranking method for genome-wide association studies. BCB Jie Liu, Elizabeth S. Burnside, Humberto J. Vidaillet, David Page
2012	J	`jnl`	Automatic classification of mammography reports by BI-RADS breast tissue composition class. J. Am. Medical Informatics Assoc. Bethany Percha, Houssam Nassif, Jafi A. Lipson, Elizabeth S. Burnside, Daniel L. Rubin
2012	—	`conf`	Extracting BI-RADS features from Portuguese clinical texts. BIBM Houssam Nassif, Filipe Cunha, Inês C. Moreira, Ricardo Cruz-Correia, Eliana Sousa, David Page, Elizabeth S. Burnside, Inês de Castro Dutra
2012	J	`jnl`	Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies CoRR Jie Liu, Chunming Zhang, Catherine A. McCarty, Peggy L. Peissig, Elizabeth S. Burnside, David Page
2012	A	`conf`	Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies. UAI Jie Liu, Chunming Zhang, Catherine A. McCarty, Peggy L. Peissig, Elizabeth S. Burnside, David Page
2012	A	`conf`	High-Dimensional Structured Feature Screening Using Binary Markov Random Fields. AISTATS Jie Liu, Chunming Zhang, Catherine A. McCarty, Peggy L. Peissig, Elizabeth S. Burnside, David Page
2012	Misc	`conf`	Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women. AMIA Houssam Nassif, Yirong Wu, David Page, Elizabeth S. Burnside
2012	—	`conf`	Relational Differential Prediction. ECML/PKDD (1) Houssam Nassif, Vítor Santos Costa, Elizabeth S. Burnside, David Page
2012	J	`jnl`	The Effect of Budgetary Restrictions on Breast Cancer Diagnostic Decisions. Manuf. Serv. Oper. Manag. Mehmet Ayvaci, Oguzhan Alagöz, Elizabeth S. Burnside
2011	—	`conf`	Predicting Malignancy from Mammography Findings and Surgical Biopsies. BIBM Pedro Ferreira, Nuno A. Fonseca, Inês de Castro Dutra, Ryan W. Woods, Elizabeth S. Burnside
2011	—	`conf`	Studying the Relevance of Breast Imaging Features. HEALTHINF Pedro Ferreira, Inês de Castro Dutra, Nuno A. Fonseca, Ryan W. Woods, Elizabeth S. Burnside
2010	J	`jnl`	Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors. Oper. Res. Jagpreet Chhatwal, Oguzhan Alagöz, Elizabeth S. Burnside
2010	—	`conf`	Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming. IHI Houssam Nassif, David Page, Mehmet Ayvaci, Jude W. Shavlik, Elizabeth S. Burnside
2010	J	`jnl`	Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer. J. Digit. Imaging Ryan W. Woods, Louis Oliphant, Kazuhiko Shinki, David Page, Jude W. Shavlik, Elizabeth S. Burnside
2009	B	`conf`	Boosting First-Order Clauses for Large, Skewed Data Sets. ILP Louis Oliphant, Elizabeth S. Burnside, Jude W. Shavlik
2009	J	`jnl`	Fluorescence Spectroscopy: An Adjunct Diagnostic Tool to Image-Guided Core Needle Biopsy of the Breast. IEEE Trans. Biomed. Eng. Changfang Zhu, Elizabeth S. Burnside, Gale A. Sisney, Lonie R. Salkowski, Josephine M. Harter, Bing Yu, Nirmala Ramanujam
2009	—	`conf`	Information Extraction for Clinical Data Mining: A Mammography Case Study. ICDM Workshops Houssam Nassif, Ryan W. Woods, Elizabeth S. Burnside, Mehmet Ayvaci, Jude W. Shavlik, David Page
2007	A*	`conf`	Change of Representation for Statistical Relational Learning. IJCAI Jesse Davis, Irene M. Ong, Jan Struyf, Elizabeth S. Burnside, David Page, Vítor Santos Costa
2005	—	`conf`	An Integrated Approach to Learning Bayesian Networks of Rules. ECML Jesse Davis, Elizabeth S. Burnside, Inês de Castro Dutra, David Page, Vítor Santos Costa
2005	Misc	`conf`	Knowledge Discovery from Structured Mammography Reports Using Inductive Logic Programming. AMIA Elizabeth S. Burnside, Jesse Davis, Vítor Santos Costa, Inês de Castro Dutra, Charles E. Kahn Jr., Jason Fine, David Page
2005	A*	`conf`	View Learning for Statistical Relational Learning: With an Application to Mammography. IJCAI Jesse Davis, Elizabeth S. Burnside, Inês de Castro Dutra, David Page, Raghu Ramakrishnan, Vítor Santos Costa, Jude W. Shavlik
2004	—	`conf`	Improving a Bayesian network's ability to predict the probability of malignancy of microcalcifications on mammography. CARS Elizabeth S. Burnside, Daniel L. Rubin, Ross D. Shachter
2004	—	`conf`	The effects of training parameters on learning a probabilistic expert system for mammography. CARS Yue Pan, Elizabeth S. Burnside
2004	—	`conf`	Using a Bayesian Network to Predict the Probability and Type of Breast Cancer Represented by Microcalcifications on Mammography. MedInfo Elizabeth S. Burnside, Daniel L. Rubin, Ross D. Shachter
2000	Misc	`conf`	A Bayesian network for mammography. AMIA Elizabeth S. Burnside, Daniel L. Rubin, Ross D. Shachter
1999	Misc	`conf`	Representing the Digital Anatomist Foundational Model as a Protege Ontology. AMIA Jin S. Hahn, Elizabeth S. Burnside, James F. Brinkley, Cornelius Rosse, Mark A. Musen