E. Burnside

发表

Boosting First-Order Clauses for Large, Skewed Data Sets

Jude W. Shavlik, Elizabeth S. Burnside, Louis Oliphant, 2009, ILP.

Development of an online, publicly accessible naive Bayesian decision support tool for mammographic mass lesions based on the American College of Radiology (ACR) BI-RADS lexicon

E. Burnside, M. Benndorf, E. Kotter, 2015, European Radiology.

Structure-Leveraged Methods in Breast Cancer Risk Prediction

Jun Fan, David Page, Yirong Wu, 2016, J. Mach. Learn. Res..

New Genetic Variants Improve Personalized Breast Cancer Diagnosis

E. Burnside, C. McCarty, A. Onitilo, 2014, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy.

C. D. Page, Terrie E. Kitchner, E. Burnside, 2016, Academic radiology.

Predicting Malignancy from Mammography Findings and Surgical Biopsies

Nuno A. Fonseca, Elizabeth S. Burnside, Inês de Castro Dutra, 2011, 2011 IEEE International Conference on Bioinformatics and Biomedicine.

The mammographic density of a mass is a significant predictor of breast cancer.

Lonie R. Salkowski, Ryan W. Woods, E. Burnside, 2011, Radiology.

Breast cancer risk estimation with artificial neural networks revisited

J. Shavlik, E. Burnside, C. E. Kahn, 2010, Cancer.

Information Extraction for Clinical Data Mining: A Mammography Case Study

David Page, Jude W. Shavlik, Mehmet Ayvaci, 2009, 2009 IEEE International Conference on Data Mining Workshops.

A logistic regression model based on the national mammography database format to aid breast cancer diagnosis.

E. Burnside, M. Lindstrom, C. E. Kahn, 2009, AJR. American journal of roentgenology.

Probabilistic computer model developed from clinical data in national mammography database format to classify mammographic findings.

C. D. Page, E. Burnside, M. Lindstrom, 2009, Radiology.

Support Vector Machines for Differential Prediction

David Page, Vítor Santos Costa, Jude W. Shavlik, 2014, ECML/PKDD.

Toward best practices in radiology reporting.

E. Burnside, C. Langlotz, C. E. Kahn, 2009, Radiology.

Learning a New View of a Database: With an Application to Mammography

Jesse Davis, Raghu Ramakrishnan, Vítor Santos Costa, 2007 .

Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming

David Page, Jude W. Shavlik, Mehmet Ayvaci, 2010, IHI.

Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms

David Page, Jude W. Shavlik, Peggy L. Peissig, 2013, AMIA.

The ACR BI-RADS experience: learning from history.

E. Burnside, C. D'Orsi, D. Rubin, 2009, Journal of the American College of Radiology : JACR.

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

Erich P Huang, E. Burnside, C. Fan, 2016, npj Breast Cancer.

Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage

Erich P Huang, E. Burnside, M. Giger, 2016, Cancer.

MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.

Erich P Huang, E. Burnside, C. Fan, 2016, Radiology.

Computer-aided diagnostic models in breast cancer screening.

E. Burnside, T. Ayer, O. Alagoz, 2010, Imaging in medicine.

A Bayesian Network to Assist Mammography Interpretation

Ross D. Shachter, E. Burnside, D. Rubin, 2005 .

The impact of alternative practices on the cost and quality of mammographic screening in the United States.

E. Burnside, L. Esserman, J. Belkora, 2001, Clinical breast cancer.

Feasibility of near-infrared diffuse optical spectroscopy on patients undergoing imageguided core-needle biopsy.

E. Burnside, Changfang Zhu, N. Ramanujam, 2007, Optics express.

Core Elements of Shared Decision-making for Women Considering Breast Cancer Screening: Results of a Modified Delphi Survey

Lori L. DuBenske, E. Burnside, J. Mahoney, 2020, Journal of General Internal Medicine.

Mammography Screening: Gaps in Patient's and Physician's Needs for Shared Decision‐Making

Lori L. DuBenske, E. Burnside, A. Trentham-Dietz, 2017, The breast journal.

Key Elements of Mammography Shared Decision-Making: a Scoping Review of the Literature

Lori L. DuBenske, E. Burnside, S. Schrager, 2018, Journal of General Internal Medicine.

Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience.

Ross D. Shachter, Jason P Fine, Daniel L Rubin, 2006, Radiology.

A probabilistic expert system that provides automated mammographic-histologic correlation: initial experience.

Ross D. Shachter, E. Burnside, D. Rubin, 2004, AJR. American journal of roentgenology.

View Learning Extended : Inventing New Tables for Statistical Relational Learning

V. S. Costa, E. Burnside, Jesse Davis, 2006 .

Axial-shear strain imaging for differentiating benign and malignant breast masses.

Tomy Varghese, Min Rao, Elizabeth S Burnside, 2010, Ultrasound in medicine & biology.

Differential value of comparison with previous examinations in diagnostic versus screening mammography.

Elizabeth S Burnside, E. Burnside, E. Sickles, 2002, AJR. American journal of roentgenology.

High-dimensional regression analysis links magnetic resonance imaging features and protein expression and signaling pathway alterations in breast invasive carcinoma

E. Burnside, K. Brandt, A. Rao, 2018, Oncoscience.

Learning Bayesian networks of rules with SAYU

Elizabeth Burnside, Jesse Davis, David Page, 2005, MRDM '05.

Differentiating Benign from Malignant Solid Breast Masses with US Strain Imaging 1

Jason P Fine, Elizabeth S Burnside, Timothy J Hall, 2007 .

A Population-Based Study of Genes Previously Implicated in Breast Cancer.

James M. Hodge, Jack A. Taylor, E. Burnside, 2021, The New England journal of medicine.

USING FINITE‐HORIZON MARKOV DECISION PROCESSES FOR OPTIMIZING POST‐MAMMOGRAPHY DIAGNOSTIC DECISIONS

E. Burnside, Sait Tunç, O. Alagoz, 2016 .

Variation in diagnostic performance among radiologists at screening CT colonography.

David H. Kim, E. Burnside, C. Hassan, 2013, Radiology.

Relationships Between Human-Extracted MRI Tumor Phenotypes of Breast Cancer and Clinical Prognostic Indicators Including Receptor Status and Molecular Subtype.

E. Burnside, M. Giger, K. Brandt, 2019, Current problems in diagnostic radiology.

American College Of Radiology/Society of Breast Imaging curriculum for resident and fellow education in breast imaging.

E. Burnside, D. Ikeda, L. Bassett, 2006, Journal of the American College of Radiology : JACR.

Effects of screening and systemic adjuvant therapy on ER-specific US breast cancer mortality.

Oguzhan Alagoz, Eveline A M Heijnsdijk, Harry J de Koning, 2014, Journal of the National Cancer Institute.

Linkage of the ACR National Mammography Database to the Network of State Cancer Registries: Proof of Concept Evaluation by the ACR National Mammography Database Committee.

E. Burnside, R. Nishikawa, D. Gur, 2019, Journal of the American College of Radiology : JACR.

A Novel Method to Assess Incompleteness of Mammography Report Content

Daniel L. Rubin, Yirong Wu, Elizabeth S. Burnside, 2014, AMIA.

A novel method to assess incompleteness of mammography reports.

Daniel L Rubin, Yirong Wu, Francisco J Gimenez, 2014, AMIA ... Annual Symposium proceedings. AMIA Symposium.

Developing a Comprehensive Database Management System for Organization and Evaluation of Mammography Datasets

Daniel L Rubin, Yirong Wu, Elizabeth S Burnside, 2014, Cancer informatics.

Leveraging Interaction between Genetic Variants and Mammographic Findings for Personalized Breast Cancer Diagnosis

E. Burnside, Yirong Wu, C. McCarty, 2015, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

An Example of Breast Cancer Regression on Imaging

E. Burnside, A. Trentham-Dietz, F. Kelcz, 2015, Radiology case reports.

Heterogeneity in Women's Adherence and Its Role in Optimal Breast Cancer Screening Policies

Turgay Ayer, Oguzhan Alagöz, Natasha K. Stout, 2016, Manag. Sci..

Using Collaborative Simulation Modeling to Develop a Web-Based Tool to Support Policy-Level Decision Making About Breast Cancer Screening Initiation Age

Oguzhan Alagoz, Natasha K. Stout, Elizabeth S. Burnside, 2017, MDM policy & practice.

Movement of a biopsy-site marker clip after completion of stereotactic directional vacuum-assisted breast biopsy: case report.

E. Burnside, E. Sickles, R. E. Sohlich, 2001, Radiology.

Association of Patient Age With Outcomes of Current-Era, Large-Scale Screening Mammography: Analysis of Data From the National Mammography Database

E. Burnside, E. Sickles, M. Bhargavan-Chatfield, 2017, JAMA oncology.

Utility of Genetic Testing in Addition to Mammography for Determining Risk of Breast Cancer Depends on Patient Age

Shara I. Feld, E. Burnside, Yirong Wu, 2018, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

Patient, faculty, and self-assessment of radiology resident performance: a 360-degree method of measuring professionalism and interpersonal/communication skills.

E. Burnside, F. Kelcz, M. Albanese, 2004, Academic radiology.

Patient and Clinician Characteristics That Predict Breast Cancer Screening Behavior in 40-49-Year-Old Women.

Lori L. DuBenske, E. Burnside, S. Schrager, 2021, Journal of patient-centered research and reviews.

Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling

David Page, Vítor Santos Costa, Jude W. Shavlik, 2013, ECML/PKDD.

Unresected Breast Cancer: Evolution of Imaging Findings Following Cryoablation

Lonie R. Salkowski, E. Burnside, F. Lee, 2015, Radiology case reports.

Image-based screening for men at high risk for breast cancer: Benefits and drawbacks.

Lonie R. Salkowski, E. Burnside, R. Strigel, 2019, Clinical imaging.

Circulating serum xenoestrogens and mammographic breast density

E. Burnside, D. Buist, A. Trentham-Dietz, 2013, Breast Cancer Research.

Risk of Breast Cancer Among Carriers of Pathogenic Variants in Breast Cancer Predisposition Genes Varies by Polygenic Risk Score

Jack A. Taylor, E. Burnside, C. Weinberg, 2021, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

Association between Screening Mammography Recall Rate and Interval Cancers in the UK Breast Cancer Service Screening Program: A Cohort Study.

E. Burnside, S. Duffy, R. Blanks, 2018, Radiology.

Pursuing optimal thresholds to recommend breast biopsy by quantifying the value of tomosynthesis

Oguzhan Alagoz, David J. Vanness, Elizabeth S. Burnside, 2014, Medical Imaging.

A new perspective on breast cancer diagnostic guidelines to reduce overdiagnosis

E. Burnside, Sait Tunç, O. Alagoz, 2018, Production and operations management.

Preference‐Sensitive Management of Post‐Mammography Decisions in Breast Cancer Diagnosis

Oguzhan Alagoz, Mehmet Eren Ahsen, Elizabeth S Burnside, 2018, Production and operations management.

Optimal Policies for Reducing Unnecessary Follow-Up Mammography Exams in Breast Cancer Diagnosis

Oguzhan Alagöz, Jagpreet Chhatwal, Elizabeth S. Burnside, 2013, Decis. Anal..

What Is the Optimal Threshold at Which to Recommend Breast Biopsy?

Oguzhan Alagoz, Jagpreet Chhatwal, Elizabeth S. Burnside, 2012, PloS one.

Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors

Oguzhan Alagöz, Jagpreet Chhatwal, Elizabeth S. Burnside, 2010, Oper. Res..

Multiple Testing under Dependence via Semiparametric Graphical Models

David Page, Chunming Zhang, Elizabeth S. Burnside, 2014, ICML.

Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies

David Page, Chunming Zhang, Peggy L. Peissig, 2012, UAI.

Screening Breast MRI Outcomes in Routine Clinical Practice: Comparison to BI-RADS Benchmarks.

Lonie R. Salkowski, Wendy B DeMartini, E. Burnside, 2017, Academic radiology.

CT Colonography Reporting and Data System (C-RADS): benchmark values from a clinical screening program.

David H. Kim, E. Burnside, P. Pickhardt, 2014, AJR. American journal of roentgenology.

Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes

Maryellen L. Giger, Karen Drukker, Gary J. Whitman, 2017, European Radiology Experimental.

Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data

Maryellen L. Giger, Elizabeth Burnside, Karen Drukker, 2015, Journal of medical imaging.

Impact of axillary ultrasound and core needle biopsy on the utility of intraoperative frozen section analysis and treatment decision making in women with invasive breast cancer.

E. Burnside, G. Sisney, L. Wilke, 2012, American journal of surgery.

Discriminatory power of common genetic variants in personalized breast cancer diagnosis

Craig K. Abbey, Jun Fan, Yirong Wu, 2016, SPIE Medical Imaging.

Learning Heterogeneous Hidden Markov Random Fields

David Page, Chunming Zhang, Elizabeth S. Burnside, 2014, AISTATS.

Protocol for a Real-Time Electronic Health Record Implementation of a Natural Language Processing and Deep Learning Clinical Decision Support Tool: A Use-Case for an Opioid Misuse Screener in Hospitalized Adults

E. Burnside, M. Leaf, C. Joyce, 2022, medRxiv.

Circulating Sex Hormones and Mammographic Breast Density among Postmenopausal Women

E. Burnside, D. Buist, A. Trentham-Dietz, 2011, Hormones & cancer.

Extracting BI-RADS features from Portuguese clinical texts

David Page, Elizabeth S. Burnside, Inês de Castro Dutra, 2012, 2012 IEEE International Conference on Bioinformatics and Biomedicine.

Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49

Jeroen J. van den Broek, E. Burnside, S. Plevritis, 2018, Medical decision making : an international journal of the Society for Medical Decision Making.

Comparative effectiveness of incorporating a hypothetical DCIS prognostic marker into breast cancer screening

E. Burnside, A. Tosteson, A. Trentham-Dietz, 2018, Breast Cancer Research and Treatment.

A Pragmatic Approach to Determine Components of Optimal Screening Mammography Practice.

E. Burnside, S. Duffy, E. Sickles, 2016, JAMA.

The National Mammography Database: Preliminary Data.

E. Burnside, P. Nagy, E. Sickles, 2016, AJR. American journal of roentgenology.

Quantifying the Benefits and Risks ofMethylphenidate as Treatment for Childhood Attention-Deficit / Hyperactivity Disorder

E. Burnside, S. Duffy, E. Sickles, 2016 .

Long-term Outcomes and Cost-effectiveness of Breast Cancer Screening with Digital Breast Tomosynthesis in the United States.

E. Burnside, E. Conant, M. Yaffe, 2019, Journal of the National Cancer Institute.

Addressing the Challenge of Assessing Physician-Level Screening Performance: Mammography as an Example

E. Burnside, D. Miglioretti, Yirong Wu, 2014, PloS one.

A National Study of the Use of Asymptomatic Systemic Imaging for Surveillance Following Breast Cancer Treatment (AFT-01)

E. Burnside, S. Edge, C. Greenberg, 2018, Annals of Surgical Oncology.

Predicting invasive breast cancer versus DCIS in different age groups

Oguzhan Alagoz, Jagpreet Chhatwal, Karla Kerlikowske, 2014, BMC Cancer.

Using automatically extracted information from mammography reports for decision-support

Selen Bozkurt, Daniel L. Rubin, Kemal Hakan Gülkesen, 2016, J. Biomed. Informatics.

Socioeconomic disparities in the decline in invasive breast cancer incidence

E. Burnside, A. Trentham-Dietz, B. Sprague, 2010, Breast Cancer Research and Treatment.

Online support: Impact on anxiety in women who experience an abnormal screening mammogram.

Lori L. DuBenske, E. Burnside, D. Gustafson, 2014, Breast.

Framing the Clinical Encounter: Shared Decision-Making, Mammography Screening, and Decision Satisfaction

Dhavan V. Shah, E. Burnside, Terry A. Little, 2020, Journal of health communication.

Modeling Ductal Carcinoma In Situ (DCIS): An Overview of CISNET Model Approaches

Jeroen J. van den Broek, E. Burnside, H. D. de Koning, 2018, Medical decision making : an international journal of the Society for Medical Decision Making.

Urinary Magnesium and Other Elements in Relation to Mammographic Breast Density, a Measure of Breast Cancer Risk

E. Burnside, M. Shafer, A. Trentham-Dietz, 2018, Nutrition and cancer.

Cancer Yield and Patterns of Follow-up for BI-RADS Category 3 after Screening Mammography Recall in the National Mammography Database.

E. Burnside, E. Sickles, R. Rosenberg, 2020, Radiology.

Age-based versus Risk-based Mammography Screening in Women 40-49 Years Old: A Cross-sectional Study.

Christina M. Shafer, E. Burnside, A. Trentham-Dietz, 2019, Radiology.

A Probabilistic Model to Support Radiologists’ Classification Decisions in Mammography Practice

Ross D. Shachter, Daniel L Rubin, Elizabeth S Burnside, 2019, Medical decision making : an international journal of the Society for Medical Decision Making.

The Effect of Budgetary Restrictions on Breast Cancer Diagnostic Decisions

Oguzhan Alagöz, Mehmet Ayvaci, Elizabeth S. Burnside, 2012, Manuf. Serv. Oper. Manag..

Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women

David Page, Yirong Wu, Elizabeth S. Burnside, 2012, AMIA.

Utility of BI-RADS Assessment Category 4 Subdivisions for Screening Breast MRI.

Lonie R. Salkowski, E. Burnside, F. Kelcz, 2017, AJR. American journal of roentgenology.

Use of microcalcification descriptors in BI-RADS 4th edition to stratify risk of malignancy.

Lonie R. Salkowski, Jason P Fine, Daniel L Rubin, 2007, Radiology.

Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making

Turgay Ayer, Elizabeth S. Burnside, Qiushi Chen, 2013, Comput. Math. Methods Medicine.

SkILL - A Stochastic Inductive Logic Learner

Ricardo Rocha, Elizabeth S. Burnside, Joana Côrte-Real, 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

Automatic classification of mammography reports by BI-RADS breast tissue composition class

Bethany Percha, Daniel L. Rubin, Elizabeth S. Burnside, 2012, J. Am. Medical Informatics Assoc..

Using machine learning to identify benign cases with non-definitive biopsy

Jude W. Shavlik, Yirong Wu, Elizabeth S. Burnside, 2013, 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013).

Comparison of screening full-field digital mammography and digital breast tomosynthesis technical recalls

Elizabeth S. Burnside, Lonie R. Salkowski, Ryan W. Woods, 2018, Other Conferences.

ACR BI-RADS Assessment Category 4 Subdivisions in Diagnostic Mammography: Utilization and Outcomes in the National Mammography Database.

E. Burnside, M. Bhargavan-Chatfield, W. DeMartini, 2018, Radiology.

Uplift Modeling with ROC: An SRL Case Study

Jude W. Shavlik, Elizabeth S. Burnside, Finn Kuusisto, 2013, ILP.

Using Multidimensional Mutual Information to Prioritize Mammographic Features for Breast Cancer Diagnosis

Yirong Wu, David J. Vanness, Elizabeth S. Burnside, 2013, AMIA.

A Comprehensive Methodology for Determining the Most Informative Mammographic Features

Yirong Wu, Oguzhan Alagöz, Mehmet Ayvaci, 2013, Journal of Digital Imaging.

Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants

David Page, Peggy L. Peissig, Elizabeth S. Burnside, 2018, AMIA.

Quantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case–control study

Mishal N. Patel, E. Burnside, S. Duffy, 2021, British Journal of Cancer.

The vitamin D pathway and mammographic breast density among postmenopausal women

E. Burnside, D. Buist, A. Trentham-Dietz, 2011, Breast Cancer Research and Treatment.

Cost benefits of picture archiving and communications systems.

H K Huang, R L Arenson, E S Burnside, 1996, Academic radiology.

Automated Diagnosis of Breast Cancer on Medical Images

Elizabeth S. Burnside, Marina Velikova, Inês de Castro Dutra, 2015, Foundations of Biomedical Knowledge Representation.

Utility of 6-month follow-up imaging after a concordant benign breast biopsy result.

Lonie R. Salkowski, E. Burnside, G. Sisney, 2011, Radiology.

Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation

Oguzhan Alagoz, Jie Liu, Yirong Wu, 2015, Journal of medical imaging.

Knowledge Discovery from Structured Mammography Reports Using Inductive Logic Programming

Charles E. Kahn, Jesse Davis, David Page, 2005, AMIA.

The use of batch reading to improve the performance of screening mammography.

E. Burnside, J. Fine, J. Park, 2005, AJR. American journal of roentgenology.

Interpretable models to predict Breast Cancer

Elizabeth S. Burnside, Inês de Castro Dutra, Rogerio Salvini, 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Informatics in radiology: comparison of logistic regression and artificial neural network models in breast cancer risk estimation.

Oguzhan Alagoz, Turgay Ayer, Jagpreet Chhatwal, 2010, Radiographics : a review publication of the Radiological Society of North America, Inc.

Developing a clinical utility framework to evaluate prediction models in radiogenomics

Oguzhan Alagoz, Elizabeth S. Burnside, Jie Liu, 2015, Medical Imaging.

Modeling the natural history of ductal carcinoma in situ based on population data

E. Burnside, H. D. de Koning, D. Weaver, 2020, Breast Cancer Research.

View Learning for Statistical Relational Learning: With an Application to Mammography

Jesse Davis, Raghu Ramakrishnan, David Page, 2005, IJCAI.

Breast Cancer Screening Strategies for Women With ATM, CHEK2, and PALB2 Pathogenic Variants: A Comparative Modeling Analysis.

James M. Hodge, Jack A. Taylor, E. Burnside, 2022, JAMA oncology.

ENIGMA CHEK2gether Project: A Comprehensive Study Identifies Functionally Impaired CHEK2 Germline Missense Variants Associated with Increased Breast Cancer Risk

James M. Hodge, E. Burnside, Y. Kamatani, 2023, Clinical cancer research : an official journal of the American Association for Cancer Research.

Using Collaborative Simulation Modeling to Develop a Web-Based Tool to Support Policy-Level Decision Making About Breast Cancer Screening Initiation Age

Jeroen J. van den Broek, E. Burnside, H. D. de Koning, 2017, MDM policy & practice.

Bayesian networks: computer-assisted diagnosis support in radiology.

Elizabeth S Burnside, E. Burnside, 2005, Academic radiology.

Using a Bayesian Network to Predict the Probability and Type of Breast Cancer Represented by Microcalcifications on Mammography

Ross D. Shachter, Daniel L. Rubin, Elizabeth S. Burnside, 2004, MedInfo.

Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports

Daniel L. Rubin, Hakan Bulu, Elizabeth S. Burnside, 2018, J. Digit. Imaging.

A history of breast cancer and older age allow risk stratification of mammographic BI-RADS 3 ratings in the diagnostic setting.

E. Burnside, M. Benndorf, Yirong Wu, 2016, Clinical imaging.

Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems

Inês Dutra, Elizabeth S. Burnside, Jude Shavlik, 2015, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

Improving a Bayesian network's ability to predict the probability of malignancy of microcalcifications on mammography

Ross D. Shachter, Daniel L. Rubin, Elizabeth S. Burnside, 2004, CARS.

A Bayesian network for mammography

Ross D. Shachter, Daniel L. Rubin, Elizabeth S. Burnside, 2000, AMIA.

Differentiating benign from malignant solid breast masses with US strain imaging.

E. Burnside, W. Svensson, T. Hall, 2007, Radiology.

Fluorescence Spectroscopy: An Adjunct Diagnostic Tool to Image-Guided Core Needle Biopsy of the Breast

Bing Yu, Elizabeth S. Burnside, Lonie R. Salkowski, 2009, IEEE Transactions on Biomedical Engineering.

Using automatically extracted information from mammography reports for decision-support

E. Burnside, D. Rubin, S. Bozkurt, 2016, J. Biomed. Informatics.

High-Dimensional Structured Feature Screening Using Binary Markov Random Fields

David Page, Chunming Zhang, Peggy L. Peissig, 2012, AISTATS.

New Genetic Variants Improve Personalized Breast Cancer Diagnosis

E. Burnside, C. McCarty, A. Onitilo, 2014, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

Axial shear strain imaging for breast mass differentiation

E. Burnside, T. Varghese, T. Hall, 2009, 2009 IEEE International Ultrasonics Symposium.

Interpreting data from audits when screening and diagnostic mammography outcomes are combined.

E. Burnside, E. Sickles, K. E. Dee, 2002, AJR. American journal of roentgenology.

Online support: Impact on anxiety in women who experience an abnormal screening mammogram.

Lori L. DuBenske, E. Burnside, D. Gustafson, 2014, Breast.

External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets.

Elmar Kotter, Mathias Langer, Elizabeth S Burnside, 2015, Medical physics.

Mammographic Breast Density and Serum Phytoestrogen Levels

E. Burnside, D. Buist, A. Trentham-Dietz, 2012, Nutrition and cancer.

Modeling ductal carcinoma in situ (DCIS) – an overview of CISNET model approaches

E. Burnside, O. Alagoz, R. Punglia, 2019 .

A utility/cost analysis of breast cancer risk prediction algorithms

Craig K. Abbey, Frank W. Samuelson, John M. Boone, 2016, SPIE Medical Imaging.

Opportunities for Operations Research in Medical Decision Making.

Sait Tunc, Oguzhan Alagoz, Elizabeth Burnside, 2014, IEEE intelligent systems.

Utility of Genetic Testing in Addition to Mammography for Determining Risk of Breast Cancer Depends on Patient Age

Shara I. Feld, E. Burnside, Yirong Wu, 2018, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

Breast Cancer Risk Prediction Using Electronic Health Records

Jun Fan, Jie Yin, David Page, 2017, 2017 IEEE International Conference on Healthcare Informatics (ICHI).

Leveraging Interaction between Genetic Variants and Mammographic Findings for Personalized Breast Cancer Diagnosis

E. Burnside, Yirong Wu, C. McCarty, 2015, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

A collective ranking method for genome-wide association studies

David Page, Elizabeth S. Burnside, Humberto J. Vidaillet, 2012, BCB.

Comparing the Value of Mammographic Features and Genetic Variants in Breast Cancer Risk Prediction

David Page, Yirong Wu, Peggy L. Peissig, 2014, AMIA.