Representing the Digital Anatomist Foundational Model as a Protege Ontology. - Jin S. Hahn; Elizabeth S. Burnside; James F. Brinkley; Cornelius Rosse; Mark A. Musen (AMIA, 1999)

A Bayesian network for mammography. - Elizabeth S. Burnside; Daniel L. Rubin; Ross D. Shachter (AMIA, 2000)

Improving a Bayesian network's ability to predict the probability of malignancy of microcalcifications on mammography. - Elizabeth S. Burnside; Daniel L. Rubin; Ross D. Shachter (CARS, 2004)

The effects of training parameters on learning a probabilistic expert system for mammography. - Yue Pan; Elizabeth S. Burnside (CARS, 2004)

Using a Bayesian Network to Predict the Probability and Type of Breast Cancer Represented by Microcalcifications on Mammography. - Elizabeth S. Burnside; Daniel L. Rubin; Ross D. Shachter (MedInfo, 2004)

Knowledge Discovery from Structured Mammography Reports Using Inductive Logic Programming. - Elizabeth S. Burnside; Jesse Davis; Vítor Santos Costa; Inês de Castro Dutra; Charles E. Kahn Jr.; Jason Fine; David Page (AMIA, 2005)

An Integrated Approach to Learning Bayesian Networks of Rules. - Jesse Davis; Elizabeth S. Burnside; Inês de Castro Dutra; David Page; Vítor Santos Costa (ECML, 2005)

View Learning for Statistical Relational Learning: With an Application to Mammography. - Jesse Davis; Elizabeth S. Burnside; Inês de Castro Dutra; David Page; Raghu Ramakrishnan 001; Vítor Santos Costa; Jude W. Shavlik (IJCAI, 2005)

Change of Representation for Statistical Relational Learning. - Jesse Davis; Irene M. Ong; Jan Struyf; Elizabeth S. Burnside; David Page; Vítor Santos Costa (IJCAI, 2007)

Information Extraction for Clinical Data Mining: A Mammography Case Study. - Houssam Nassif; Ryan W. Woods; Elizabeth S. Burnside; Mehmet Ayvaci; Jude W. Shavlik; David Page (ICDM Workshops, 2009)

Boosting First-Order Clauses for Large, Skewed Data Sets. - Louis Oliphant; Elizabeth S. Burnside; Jude W. Shavlik (ILP, 2009)

Fluorescence Spectroscopy: An Adjunct Diagnostic Tool to Image-Guided Core Needle Biopsy of the Breast. - Changfang Zhu; Elizabeth S. Burnside; Gale A. Sisney; Lonie R. Salkowski; Josephine M. Harter; Bing Yu; Nirmala Ramanujam (IEEE Trans. Biomed. Eng., 2009)

Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming. - Houssam Nassif; David Page; Mehmet Ayvaci; Jude W. Shavlik; Elizabeth S. Burnside (IHI, 2010)

Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors. - Jagpreet Chhatwal; Oguzhan Alagöz; Elizabeth S. Burnside (Oper. Res., 2010)

Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer. - Ryan W. Woods; Louis Oliphant; Kazuhiko Shinki; David Page; Jude W. Shavlik; Elizabeth S. Burnside (J. Digit. Imaging, 2010)

Predicting Malignancy from Mammography Findings and Surgical Biopsies. - Pedro Ferreira 002; Nuno A. Fonseca; Inês de Castro Dutra; Ryan W. Woods; Elizabeth S. Burnside (BIBM, 2011)

Studying the Relevance of Breast Imaging Features. - Pedro Ferreira 002; Inês de Castro Dutra; Nuno A. Fonseca; Ryan W. Woods; Elizabeth S. Burnside (HEALTHINF, 2011)

Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women. - Houssam Nassif; Yirong Wu; David Page; Elizabeth S. Burnside (AMIA, 2012)

A collective ranking method for genome-wide association studies. - Jie Liu 006; Elizabeth S. Burnside; Humberto J. Vidaillet; David Page (BCB, 2012)

Extracting BI-RADS features from Portuguese clinical texts. - Houssam Nassif; Filipe Cunha; Inês C. Moreira; Ricardo Cruz-Correia; Eliana Sousa; David Page; Elizabeth S. Burnside; Inês de Castro Dutra (BIBM, 2012)

Relational Differential Prediction. - Houssam Nassif; Vítor Santos Costa; Elizabeth S. Burnside; David Page (ECML/PKDD (1), 2012)

Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies. - Jie Liu 006; Chunming Zhang; Catherine A. McCarty; Peggy L. Peissig; Elizabeth S. Burnside; David Page (UAI, 2012)

Automatic classification of mammography reports by BI-RADS breast tissue composition class. - Bethany Percha; Houssam Nassif; Jafi A. Lipson; Elizabeth S. Burnside; Daniel L. Rubin (J. Am. Medical Informatics Assoc., 2012)

High-Dimensional Structured Feature Screening Using Binary Markov Random Fields. - Jie Liu 006; Chunming Zhang; Catherine A. McCarty; Peggy L. Peissig; Elizabeth S. Burnside; David Page (AISTATS, 2012)

The Effect of Budgetary Restrictions on Breast Cancer Diagnostic Decisions. - Mehmet Ayvaci; Oguzhan Alagöz; Elizabeth S. Burnside (Manuf. Serv. Oper. Manag., 2012)

Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms. - Jie Liu 006; David Page; Houssam Nassif; Jude W. Shavlik; Peggy L. Peissig; Catherine A. McCarty; Adedayo A. Onitilo; Elizabeth S. Burnside (AMIA, 2013)

Using Multidimensional Mutual Information to Prioritize Mammographic Features for Breast Cancer Diagnosis. - Yirong Wu; David J. Vanness; Elizabeth S. Burnside (AMIA, 2013)

Using machine learning to identify benign cases with non-definitive biopsy. - Finn Kuusisto; Inês de Castro Dutra; Houssam Nassif; Yirong Wu; Molly E. Klein; Heather B. Neuman; Jude W. Shavlik; Elizabeth S. Burnside (Healthcom, 2013)

Uplift Modeling with ROC: An SRL Case Study. - Houssam Nassif; Finn Kuusisto; Elizabeth S. Burnside; Jude W. Shavlik (ILP (Late Breaking Papers), 2013)

Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling. - Houssam Nassif; Finn Kuusisto; Elizabeth S. Burnside; David Page; Jude W. Shavlik; Vítor Santos Costa (ECML/PKDD (3), 2013)

Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making. - Turgay Ayer; Qiushi Chen; Elizabeth S. Burnside (Comput. Math. Methods Medicine, 2013)

Optimal Policies for Reducing Unnecessary Follow-Up Mammography Exams in Breast Cancer Diagnosis. - Oguzhan Alagöz; Jagpreet Chhatwal; Elizabeth S. Burnside (Decis. Anal., 2013)

A Comprehensive Methodology for Determining the Most Informative Mammographic Features. - Yirong Wu; Oguzhan Alagöz; Mehmet Ayvaci; Alejandro Munoz del Rio; David J. Vanness; Ryan S. Woods; Elizabeth S. Burnside (J. Digit. Imaging, 2013)

Learning Heterogeneous Hidden Markov Random Fields. - Jie Liu 006; Chunming Zhang; Elizabeth S. Burnside; David Page (AISTATS, 2014)

A Novel Method to Assess Incompleteness of Mammography Report Content. - Francisco Gimenez; Yirong Wu; Elizabeth S. Burnside; Daniel L. Rubin (AMIA, 2014)

Comparing the Value of Mammographic Features and Genetic Variants in Breast Cancer Risk Prediction. - Yirong Wu; Jie Liu 006; David Page; Peggy L. Peissig; Catherine A. McCarty; Adedayo A. Onitilo; Elizabeth S. Burnside (AMIA, 2014)

Multiple Testing under Dependence via Semiparametric Graphical Models. - Jie Liu 006; Chunming Zhang; Elizabeth S. Burnside; David Page (ICML, 2014)

Expert Bayes: Automatically Refining Manually Built Bayesian Networks. - Ezilda Almeida; Pedro Ferreira 002; Tiago T. V. Vinhoza; Inês de Castro Dutra; Paulo Vinicius Koerich Borges; Yirong Wu; Elizabeth S. Burnside (ICMLA, 2014)

Support Vector Machines for Differential Prediction. - Finn Kuusisto; Vítor Santos Costa; Houssam Nassif; Elizabeth S. Burnside; David Page; Jude W. Shavlik (ECML/PKDD (2), 2014)

ExpertBayes: Automatically refining manually built Bayesian networks. - Ezilda Almeida; Pedro Ferreira 002; Tiago T. V. Vinhoza; Inês de Castro Dutra; Jingwei Li; Yirong Wu; Elizabeth S. Burnside (CoRR, 2014)

Healthcare Intelligence: Turning Data into Knowledge. - Hui Yang 003; O. Erhun Kundakcioglu; Jing Li 016; Teresa Wu; Joseph Ross Mitchell; Amy K. Hara; William Pavlicek; Leland S. Hu; Alvin C. Silva; Christine M. Zwart; Sait Tunç; Oguzhan Alagöz; et al. (IEEE Intell. Syst., 2014)

Automated Diagnosis of Breast Cancer on Medical Images. - Marina Velikova; Inês Dutra; Elizabeth S. Burnside (Foundations of Biomedical Knowledge Representation, 2015)

SkILL - A Stochastic Inductive Logic Learner. - Joana Côrte-Real; Theofrastos Mantadelis; Inês de Castro Dutra; Ricardo Rocha 001; Elizabeth S. Burnside (ICMLA, 2015)

Predicting malignancy from mammography findings and image-guided core biopsies. - Pedro Ferreira 002; Nuno A. Fonseca; Inês de Castro Dutra; Ryan W. Woods; Elizabeth S. Burnside (Int. J. Data Min. Bioinform., 2015)

Interpretable models to predict Breast Cancer. - Pedro Ferreira 002; Inês Dutra; Rogerio Salvini; Elizabeth S. Burnside (BIBM, 2016)

A Speech-to-Text Interface for MammoClass. - Ricardo Sousa Rocha; Pedro Ferreira 002; Inês de Castro Dutra; Ricardo Cruz-Correia; Rogerio Salvini; Elizabeth S. Burnside (CBMS, 2016)

A utility/cost analysis of breast cancer risk prediction algorithms. - Craig K. Abbey; Yirong Wu; Elizabeth S. Burnside; Adam Wunderlich; Frank W. Samuelson; John M. Boone (Image Perception, Observer Performance, and Technology Assessment, 2016)

Discriminatory power of common genetic variants in personalized breast cancer diagnosis. - Yirong Wu; Craig K. Abbey; Jie Liu 006; Irene M. Ong; Peggy L. Peissig; Adedayo A. Onitilo; Jun Fan; Ming Yuan 001; Elizabeth S. Burnside (Image Perception, Observer Performance, and Technology Assessment, 2016)

Using automatically extracted information from mammography reports for decision-support. - Selen Bozkurt; Francisco Gimenez; Elizabeth S. Burnside; Kemal Hakan Gülkesen; Daniel L. Rubin (J. Biomed. Informatics, 2016)

Structure-Leveraged Methods in Breast Cancer Risk Prediction. - Jun Fan; Yirong Wu; Ming Yuan 001; David Page; Jie Liu 006; Irene M. Ong; Peggy L. Peissig; Elizabeth S. Burnside (J. Mach. Learn. Res., 2016)

Heterogeneity in Women's Adherence and Its Role in Optimal Breast Cancer Screening Policies. - Turgay Ayer; Oguzhan Alagöz; Natasha K. Stout; Elizabeth S. Burnside (Manag. Sci., 2016)

Breast Cancer Risk Prediction Using Electronic Health Records. - Yirong Wu; Elizabeth S. Burnside; Jennifer Cox; Jun Fan; Ming Yuan 001; Jie Yin; Peggy L. Peissig; Alexander G. Cobian; David Page; Mark W. Craven (ICHI, 2017)

Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants. - Shara Feld; Kaitlin M. Woo; Roxana Alexandridis; Yirong Wu; Jie Liu 006; Peggy L. Peissig; Adedayo A. Onitilo; Jennifer Cox; David Page; Elizabeth S. Burnside (AMIA, 2018)

Comparison of screening full-field digital mammography and digital breast tomosynthesis technical recalls. - Lonie R. Salkowski; Mai Elezaby; Amy M. Fowler; Elizabeth S. Burnside; Ryan W. Woods; Roberta M. Strigel (IWBI, 2018)

Quantifying predictive capability of electronic health records for the most harmful breast cancer. - Yirong Wu; Jun Fan; Peggy L. Peissig; Richard L. Berg; Ahmad Pahlavan Tafti; Jie Yin; Ming Yuan 001; David Page; Jennifer Cox; Elizabeth S. Burnside (Image Perception, Observer Performance, and Technology Assessment, 2018)

Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports. - Hakan Bulu; Dorothy A. Sippo; Janie M. Lee; Elizabeth S. Burnside; Daniel L. Rubin (J. Digit. Imaging, 2018)