Scholars@Duke

• Background
• 

  Education, Training, & Certifications

  • Ph.D., University of Illinois 1993
• 

  Previous Appointments & Affiliations

  • Instructor in the Department of Biostatistics & Bioinformatics, Biostatistics & Bioinformatics, Basic Science Departments 2019 - 2021
• Recognition
• 

  In the News

  • MAY 4, 2023

    Duke Today

    Duke Awards 44 Distinguished Professorships

  • FEB 21, 2023

    Duke Today

    ChatGPT Is Here to Stay. What Do We Do With It?
• 

  Awards & Honors

  • Fellow of ACMI 2021. American Medical Informatics Association. October 2021
• Research
• 

  Selected Grants

  • Machine Learning Methods to Develop and Deploy Real-Time Risk Surveillance for Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder from the Electronic Health Record awarded by National Institutes of Health 2022 - 2027
  • Understanding heterogeneity and Improving the Precision of Prostate Cancer Clinical Outcomes in the Modern Era awarded by Department of Defense 2023 - 2026
  • Design, prediction, and prioritization of systematic perturbations of the human genome awarded by National Institutes of Health 2021 - 2026
  • Machine-learning & Analytics for Maternal-health Interventions (MAMI): A Cross-CTSA Collaboration awarded by University of North Carolina - Chapel Hill 2022 - 2026
  • Duke Center for Advancement of Child Health (CAtCH). awarded by National Institutes of Health 2021 - 2026
  • Advancing the Measurement and Classification of Lower Urinary Tract Dysfunction awarded by National Institutes of Health 2012 - 2024
  • Development of a program to assess and treat distress in glaucoma patients using an automated EHR-derived AI algorithm awarded by National Institutes of Health 2021 - 2023
  • NSF Convergence Accelerator Track D: A Trusted Integrative Model and Data Sharing Platform for Accelerating AI-Driven Health Innovation awarded by  National Science Foundation 2020 - 2022
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  External Relationships

  • Amazon, Inc
• Publications & Artistic Works
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  Selected Publications

  • Books

    • Page, D. Preface. Vol. 1446, 1998.

  • Academic Articles

    • Berchuck, Samuel I., Alessandro A. Jammal, David Page, Tamara J. Somers, and Felipe A. Medeiros. “A Framework for Automating Psychiatric Distress Screening in Ophthalmology Clinics Using an EHR-Derived AI Algorithm.” Transl Vis Sci Technol 11, no. 10 (October 3, 2022): 6. https://doi.org/10.1167/tvst.11.10.6.
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    • Carroll, Molly J., Katja Kaipio, Johanna Hynninen, Olli Carpen, Sampsa Hautaniemi, David Page, and Pamela K. Kreeger. “A Subset of Secreted Proteins in Ascites Can Predict Platinum-Free Interval in Ovarian Cancer.” Cancers (Basel) 14, no. 17 (September 1, 2022). https://doi.org/10.3390/cancers14174291.
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    • Movaghar, Arezoo, David Page, Murray Brilliant, and Marsha Mailick. “Advancing artificial intelligence-assisted pre-screening for fragile X syndrome.” Bmc Med Inform Decis Mak 22, no. 1 (June 10, 2022): 152. https://doi.org/10.1186/s12911-022-01896-5.
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    • Movaghar, Arezoo, David Page, Murray Brilliant, and Marsha Mailick. “Response to Timothé Ménard.” Genet Med 24, no. 3 (March 2022): 752–53. https://doi.org/10.1016/j.gim.2021.10.023.
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    • Movaghar, Arezoo, David Page, Murray Brilliant, and Marsha Mailick. “Prevalence of Underdiagnosed Fragile X Syndrome in 2 Health Systems.” Jama Netw Open 4, no. 12 (December 1, 2021): e2141516. https://doi.org/10.1001/jamanetworkopen.2021.41516.
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    • Huang, Xiayuan, Nicholas Tatonetti, Katie LaRow, Brooke Delgoffee, John Mayer, David Page, and Scott J. Hebbring. “E-Pedigrees: a large-scale automatic family pedigree prediction application.” Bioinformatics 37, no. 21 (November 5, 2021): 3966–68. https://doi.org/10.1093/bioinformatics/btab419.
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    • Movaghar, Arezoo, David Page, Krishanu Saha, Moira Rynn, and Jan Greenberg. “Machine learning approach to measurement of criticism: The core dimension of expressed emotion.” J Fam Psychol 35, no. 7 (October 2021): 1007–15. https://doi.org/10.1037/fam0000906.
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    • Movaghar, Arezoo, David Page, Danielle Scholze, Jinkuk Hong, Leann Smith DaWalt, Finn Kuusisto, Ron Stewart, Murray Brilliant, and Marsha Mailick. “Artificial intelligence-assisted phenotype discovery of fragile X syndrome in a population-based sample.” Genet Med 23, no. 7 (July 2021): 1273–80. https://doi.org/10.1038/s41436-021-01144-7.
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    • Olsen, Cameron R., Robert J. Mentz, Kevin J. Anstrom, David Page, and Priyesh A. Patel. “Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure.” Am Heart J 229 (November 2020): 1–17. https://doi.org/10.1016/j.ahj.2020.07.009.
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    • Wang, Fan, Richard L. Schilsky, David Page, Robert M. Califf, Kei Cheung, Xiaofei Wang, and Herbert Pang. “Development and Validation of a Natural Language Processing Tool to Generate the CONSORT Reporting Checklist for Randomized Clinical Trials.” Jama Netw Open 3, no. 10 (October 1, 2020): e2014661. https://doi.org/10.1001/jamanetworkopen.2020.14661.
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    • Kuusisto, Finn, Daniel Ng, John Steill, Ian Ross, Miron Livny, James Thomson, David Page, and Ron Stewart. “KinderMiner Web: a simple web tool for ranking pairwise associations in biomedical applications.” F1000res 9 (2020): 832. https://doi.org/10.12688/f1000research.25523.2.
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    • Kuusisto, Finn, Vitor Santos Costa, Zhonggang Hou, James Thomson, David Page, and Ron Stewart. “Machine learning to predict developmental neurotoxicity with high-throughput data from 2D bio-engineered tissues.” Proc Int Conf Mach Learn Appl 2019 (December 2019): 293–98. https://doi.org/10.1109/icmla.2019.00055.
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    • Carroll, Molly J., Carl R. Parent, David Page, and Pamela K. Kreeger. “Tumor cell sensitivity to vemurafenib can be predicted from protein expression in a BRAF-V600E basket trial setting.” Bmc Cancer 19, no. 1 (October 31, 2019): 1025. https://doi.org/10.1186/s12885-019-6175-2.
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    • Geng, S., Z. Kuang, P. L. Peissig, D. Page, L. Maursetter, and K. E. Hansen. “Parathyroid hormone independently predicts fracture, vascular events, and death in patients with stage 3 and 4 chronic kidney disease.” Osteoporos Int 30, no. 10 (October 2019): 2019–25. https://doi.org/10.1007/s00198-019-05033-3.
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    • Movaghar, Arezoo, David Page, Murray Brilliant, Mei Wang Baker, Jan Greenberg, Jinkuk Hong, Leann Smith DaWalt, et al. “Data-driven phenotype discovery of FMR1 premutation carriers in a population-based sample.” Sci Adv 5, no. 8 (August 2019): eaaw7195. https://doi.org/10.1126/sciadv.aaw7195.
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    • Page, David, Finn kuusisto, John steill, Zhaobin Kuang, James Thomson, and Ron Stewart. “A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical Applications.” Amia Summits on Translational Science Proceeding, July 26, 2019, 166–74.
    • Patterson, Brian W., Collin J. Engstrom, Varun Sah, Maureen A. Smith, Eneida A. Mendonça, Michael S. Pulia, Michael D. Repplinger, Azita G. Hamedani, David Page, and Manish N. Shah. “Training and Interpreting Machine Learning Algorithms to Evaluate Fall Risk After Emergency Department Visits.” Med Care 57, no. 7 (July 2019): 560–66. https://doi.org/10.1097/MLR.0000000000001140.
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    • Badger, Jonathan, Eric LaRose, John Mayer, Fereshteh Bashiri, David Page, and Peggy Peissig. “Machine learning for phenotyping opioid overdose events.” J Biomed Inform 94 (June 2019): 103185. https://doi.org/10.1016/j.jbi.2019.103185.
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    • Dhami, Devendra Singh, Gautam Kunapuli, Mayukh Das, David Page, and Sriraam Natarajan. “Drug-Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities.” Smart Health (Amst) 9–10 (December 2018): 88–100. https://doi.org/10.1016/j.smhl.2018.07.007.
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    • Hansen, Richard A., Ning Cheng, Md Motiur Rahman, Yasser Alatawi, Jingjing Qian, Peggy L. Peissig, Richard L. Berg, and C David Page. “Authors' Reply to Courtney Suggs and Colleagues' Comment on: "Mixed Approach Retrospective Analyses of Suicide and Suicidal Ideation for Brand Compared with Generic Central Nervous System Drugs".” Drug Saf 41, no. 12 (December 2018): 1423–24. https://doi.org/10.1007/s40264-018-0728-1.
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    • Giacomelli, Irene, Somesh Jha, Ross Kleiman, David Page, and Kyonghwan Yoon. “Privacy-Preserving Collaborative Prediction using Random Forests,” November 21, 2018.
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    • Bishop-Fitzpatrick, Lauren, Arezoo Movaghar, Jan S. Greenberg, David Page, Leann S. DaWalt, Murray H. Brilliant, and Marsha R. Mailick. “Using machine learning to identify patterns of lifetime health problems in decedents with autism spectrum disorder.” Autism Res 11, no. 8 (August 2018): 1120–28. https://doi.org/10.1002/aur.1960.
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    • Hao, Ling, Jingxin Wang, David Page, Sanjay Asthana, Henrik Zetterberg, Cynthia Carlsson, Ozioma C. Okonkwo, and Lingjun Li. “Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer's Disease.” Sci Rep 8, no. 1 (June 18, 2018): 9291. https://doi.org/10.1038/s41598-018-27031-x.
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    • Alatawi, Y., Md M. Rahman, N. Cheng, J. Qian, P. L. Peissig, R. L. Berg, C. D. Page, and R. A. Hansen. “Brand vs generic adverse event reporting patterns: An authorized generic-controlled evaluation of cardiovascular medications.” J Clin Pharm Ther 43, no. 3 (June 2018): 327–35. https://doi.org/10.1111/jcpt.12646.
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    • Cheng, Ning, Md Motiur Rahman, Yasser Alatawi, Jingjing Qian, Peggy L. Peissig, Richard L. Berg, C David Page, and Richard A. Hansen. “Mixed Approach Retrospective Analyses of Suicide and Suicidal Ideation for Brand Compared with Generic Central Nervous System Drugs.” Drug Saf 41, no. 4 (April 2018): 363–76. https://doi.org/10.1007/s40264-017-0624-0.
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    • Huang, Xiayuan, Robert C. Elston, Guilherme J. Rosa, John Mayer, Zhan Ye, Terrie Kitchner, Murray H. Brilliant, David Page, and Scott J. Hebbring. “Applying family analyses to electronic health records to facilitate genetic research.” Bioinformatics 34, no. 4 (February 15, 2018): 635–42. https://doi.org/10.1093/bioinformatics/btx569.
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    • Hansen, R. A., J. Qian, R. L. Berg, J. G. Linneman, E. Seoane-Vazquez, S. Dutcher, S. Raofi, C. D. Page, and P. L. Peissig. “Comparison of Outcomes Following a Switch From a Brand to an Authorized Versus Independent Generic Drug.” Clin Pharmacol Ther 103, no. 2 (February 2018): 310–17. https://doi.org/10.1002/cpt.591.
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    • Feld, Shara I., Kaitlin M. Woo, Roxana Alexandridis, Yirong Wu, Jie Liu, Peggy Peissig, Adedayo A. Onitilo, Jennifer Cox, C David Page, and Elizabeth S. Burnside. “Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants.” Amia Annu Symp Proc 2018 (2018): 1253–62.
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    • Kalscheur, Matthew M., Ryan T. Kipp, Matthew C. Tattersall, Chaoqun Mei, Kevin A. Buhr, David L. DeMets, Michael E. Field, Lee L. Eckhardt, and C David Page. “Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial.” Circ Arrhythm Electrophysiol 11, no. 1 (January 2018): e005499. https://doi.org/10.1161/CIRCEP.117.005499.
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    • Mailick, Marsha R., Arezoo Movaghar, Jinkuk Hong, Jan S. Greenberg, Leann S. DaWalt, Lili Zhou, Jonathan Jackson, et al. “Health Profiles of Mosaic Versus Non-mosaic FMR1 Premutation Carrier Mothers of Children With Fragile X Syndrome.” Front Genet 9 (2018): 173. https://doi.org/10.3389/fgene.2018.00173.
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    • Rahman, Md Motiur, Yasser Alatawi, Ning Cheng, Jingjing Qian, Peggy L. Peissig, Richard L. Berg, David C. Page, and Richard A. Hansen. “Methodological Considerations for Comparison of Brand Versus Generic Versus Authorized Generic Adverse Event Reports in the US Food and Drug Administration Adverse Event Reporting System (FAERS).” Clin Drug Investig 37, no. 12 (December 2017): 1143–52. https://doi.org/10.1007/s40261-017-0574-4.
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    • Tafti, A. P., J. Badger, E. LaRose, E. Shirzadi, A. Mahnke, J. Mayer, Z. Ye, D. Page, and P. Peissig. “Adverse drug event discovery using biomedical literature: A big data neural network adventure.” Jmir Medical Informatics 5, no. 4 (October 1, 2017). https://doi.org/10.2196/medinform.9170.
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    • Rahman, Md Motiur, Yasser Alatawi, Ning Cheng, Jingjing Qian, Annya V. Plotkina, Peggy L. Peissig, Richard L. Berg, David Page, and Richard A. Hansen. “Comparison of brand versus generic antiepileptic drug adverse event reporting rates in the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS).” Epilepsy Res 135 (September 2017): 71–78. https://doi.org/10.1016/j.eplepsyres.2017.06.007.
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    • Page, David, Devendra Dhami, Ameet Soni, and Sriraam Natarajan. “Identifying Parkinson's Patients: A functional Gradient Boosting Approach.” Artificial Intelligence in Medicine, June 2017, 332–37.
    • Natarajan, Sriraam, Vishal Bangera, Tushar Khot, Jose Picado, Anurag Wazalwar, Vitor Santos Costa, David Page, and Michael Caldwell. “Markov Logic Networks for Adverse Drug Event Extraction from Text.” Knowl Inf Syst 51, no. 2 (May 2017): 435–57. https://doi.org/10.1007/s10115-016-0980-6.
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    • Hansen, Richard A., Jingjing Qian, Richard Berg, James Linneman, Enrique Seoane-Vazquez, Sarah K. Dutcher, Saeid Raofi, C David Page, and Peggy Peissig. “Comparison of Generic-to-Brand Switchback Rates Between Generic and Authorized Generic Drugs.” Pharmacotherapy 37, no. 4 (April 2017): 429–37. https://doi.org/10.1002/phar.1908.
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    • Geng, Sinong, Zhaobin Kuang, and David Page. “An Efficient Pseudo-likelihood Method for Sparse Binary Pairwise Markov Network Estimation,” February 27, 2017.
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    • Giacomelli, Irene, Somesh Jha, C David Page, and Kyonghwan Yoon. “Privacy-Preserving Ridge Regression on Distributed Data.” Iacr Cryptol. Eprint Arch. 2017 (2017): 707–707.
    • Giacomelli, Irene, Somesh Jha, Marc Joye, C David Page, and Kyonghwan Yoon. “Privacy-Preserving Ridge Regression with only Linearly-Homomorphic Encryption.” Iacr Cryptol. Eprint Arch. 2017 (2017): 979–979.
    • Fan, Jun, Yirong Wu, Ming Yuan, David Page, Jie Liu, Irene M. Ong, Peggy Peissig, and Elizabeth Burnside. “Structure-Leveraged Methods in Breast Cancer Risk Prediction.” J Mach Learn Res 17 (December 2016).
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    • Liu, J., C. Zhang, and D. Page. “Multiple testing under dependence via graphical models.” Annals of Applied Statistics 10, no. 3 (September 1, 2016): 1699–1724. https://doi.org/10.1214/16-AOAS956.
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    • Hao, Ling, Tyler Greer, David Page, Yatao Shi, Chad M. Vezina, Jill A. Macoska, Paul C. Marker, et al. “In-Depth Characterization and Validation of Human Urine Metabolomes Reveal Novel Metabolic Signatures of Lower Urinary Tract Symptoms.” Sci Rep 6 (August 9, 2016): 30869. https://doi.org/10.1038/srep30869.
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    • Fan, J., Y. Wu, M. Yuan, D. Page, J. Liu, I. M. Ong, P. Peissig, and E. Burnside. “Structure-leveraged methods in breast cancer risk prediction.” Journal of Machine Learning Research 17 (May 1, 2016).
    • Fan, J., Y. Wu, M. Yuan, D. Page, J. Liu, I. M. Ong, P. Peissig, and E. Burnside. “Structure-leveraged methods in breast cancer risk prediction.” Journal of Machine Learning Research 17 (May 1, 2016).
    • Burnside, Elizabeth S., Jie Liu, Yirong Wu, Adedayo A. Onitilo, Catherine A. McCarty, C David Page, Peggy L. Peissig, et al. “Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy.” Acad Radiol 23, no. 1 (January 2016): 62–69. https://doi.org/10.1016/j.acra.2015.09.007.
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    • Craven, M., and C. D. Page. “Big data in healthcare: Opportunities and challenges.” Big Data 3, no. 4 (December 1, 2015): 209–10. https://doi.org/10.1089/big.2015.29001.mcr.
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    • Schwartz, Michael P., Zhonggang Hou, Nicholas E. Propson, Jue Zhang, Collin J. Engstrom, Vitor Santos Costa, Peng Jiang, et al. “Human pluripotent stem cell-derived neural constructs for predicting neural toxicity.” Proc Natl Acad Sci U S A 112, no. 40 (October 6, 2015): 12516–21. https://doi.org/10.1073/pnas.1516645112.
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    • Wu, Y., C. K. Abbey, X. Chen, J. Liu, D. C. Page, O. Alagoz, P. Peissig, A. A. Onitilo, and E. S. Burnside. “Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation.” Journal of Medical Imaging 2, no. 4 (October 1, 2015). https://doi.org/10.1117/1.JMI.2.4.041005.
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    • Ye, Zhan, John Mayer, Lynn Ivacic, Zhiyi Zhou, Min He, Steven J. Schrodi, David Page, Murray H. Brilliant, and Scott J. Hebbring. “Phenome-wide association studies (PheWASs) for functional variants.” Eur J Hum Genet 23, no. 4 (April 2015): 523–29. https://doi.org/10.1038/ejhg.2014.123.
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    • Chang, Timothy S., Ronald E. Gangnon, C. David Page, William R. Buckingham, Aman Tandias, Kelly J. Cowan, Carrie D. Tomasallo, Brian G. Arndt, Lawrence P. Hanrahan, and Theresa W. Guilbert. “Sparse modeling of spatial environmental variables associated with asthma.” Journal of Biomedical Informatics 53 (February 2015): 320–29. https://doi.org/10.1016/j.jbi.2014.12.005.
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    • Davis, J., V. S. Costa, P. Peissig, M. Caldwell, and D. Page. “Predicting adverse drug events from electronic medical records.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9521 LNCS (January 1, 2015): 243–57. https://doi.org/10.1007/978-3-319-28007-3_16.
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    • Weiss, Jeremy, Finn Kuusisto, Kendrick Boyd, Jie Liu, and David Page. “Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution.” Amia Annu Symp Proc 2015 (2015): 1306–15.
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    • Peissig, Peggy L., Vitor Santos Costa, Michael D. Caldwell, Carla Rottscheit, Richard L. Berg, Eneida A. Mendonca, and David Page. “Relational machine learning for electronic health record-driven phenotyping.” J Biomed Inform 52 (December 2014): 260–70. https://doi.org/10.1016/j.jbi.2014.07.007.
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    • Chang, Timothy S., Robert F. Lemanske, David T. Mauger, Anne M. Fitzpatrick, Christine A. Sorkness, Stanley J. Szefler, Ronald E. Gangnon, C David Page, Daniel J. Jackson, and Daniel J. Childhood Asthma Research and Education (CARE) Network Investigators. “Childhood asthma clusters and response to therapy in clinical trials.” J Allergy Clin Immunol 133, no. 2 (February 2014): 363–69. https://doi.org/10.1016/j.jaci.2013.09.002.
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    • Shahinfar, Saleh, David Page, Jerry Guenther, Victor Cabrera, Paul Fricke, and Kent Weigel. “Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms.” J Dairy Sci 97, no. 2 (February 2014): 731–42. https://doi.org/10.3168/jds.2013-6693.
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    • Page, David, Jie Liu, Peggy Peissing, Catherine McCarty, Adedayo Onitilo, Amy Trentham-Dietz, and Elizabeth Burnside. “New Genetic Variants Improve Personalized Breast Cancer Diagnosis.” Amia Summits on Translational Science Proceedings, 2014, 83–89.
    • Wu, Yirong, Jie Liu, David Page, Peggy Peissig, Catherine McCarty, Adedayo A. Onitilo, and Elizabeth S. Burnside. “Comparing the value of mammographic features and genetic variants in breast cancer risk prediction.” Amia Annu Symp Proc 2014 (2014): 1228–37.
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    • Zeng, C., E. Lantz, J. F. Naughton, and D. Page. “On differentially private inductive logic programming.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8812 (January 1, 2014): 18–30. https://doi.org/10.1007/978-3-662-44923-3_2.
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    • Roetker, Nicholas S., C David Page, James A. Yonker, Vicky Chang, Carol L. Roan, Pamela Herd, Taissa S. Hauser, Robert M. Hauser, and Craig S. Atwood. “Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms.” Am J Public Health 103 Suppl 1, no. Suppl 1 (October 2013): S136–44. https://doi.org/10.2105/AJPH.2012.301141.
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    • Yao, C., D. M. Spurlock, L. E. Armentano, C. D. Page, M. J. VandeHaar, D. M. Bickhart, and K. A. Weigel. “Random Forests approach for identifying additive and epistatic single nucleotide polymorphisms associated with residual feed intake in dairy cattle.” J Dairy Sci 96, no. 10 (October 2013): 6716–29. https://doi.org/10.3168/jds.2012-6237.
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    • Hebbring, S. J., S. J. Schrodi, Z. Ye, Z. Zhou, D. Page, and M. H. Brilliant. “A PheWAS approach in studying HLA-DRB1*1501.” Genes Immun 14, no. 3 (April 2013): 187–91. https://doi.org/10.1038/gene.2013.2.
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    • Chang, Timothy S., Robert F. Lemanske, Theresa W. Guilbert, James E. Gern, Michael H. Coen, Michael D. Evans, Ronald E. Gangnon, C. David Page, and Daniel J. Jackson. “Evaluation of the modified asthma predictive index in high-risk preschool children.” J Allergy Clin Immunol Pract 1, no. 2 (March 2013): 152–56. https://doi.org/10.1016/j.jaip.2012.10.008.
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    • Liu, Jie, David Page, Houssam Nassif, Jude Shavlik, Peggy Peissig, Catherine McCarty, Adedayo A. Onitilo, and Elizabeth Burnside. “Genetic variants improve breast cancer risk prediction on mammograms.” Amia Annu Symp Proc 2013 (2013): 876–85.
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    • Hou, Zhonggang, Jue Zhang, Michael P. Schwartz, Ron Stewart, C David Page, William L. Murphy, and James A. Thomson. “A human pluripotent stem cell platform for assessing developmental neural toxicity screening.” Stem Cell Res Ther 4 Suppl 1, no. Suppl 1 (2013): S12. https://doi.org/10.1186/scrt373.
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    • Boyd, Kendrick, Vítor Santos Costa, Jesse Davis, and C David Page. “Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation.” Proc Int Conf Mach Learn 2012 (December 1, 2012): 349.
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    • A Santos, Jose C., Houssam Nassif, David Page, Stephen H. Muggleton, and Michael J. E Sternberg. “Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study.” Bmc Bioinformatics 13 (July 11, 2012): 162. https://doi.org/10.1186/1471-2105-13-162.
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    • Nassif, Houssam, Filipe Cunha, Inês C. Moreira, Ricardo Cruz-Correia, Eliana Sousa, David Page, Elizabeth Burnside, and Inês Dutra. “Extracting BI-RADS Features from Portuguese Clinical Texts.” Proceedings (Ieee Int Conf Bioinformatics Biomed), 2012, 1–4. https://doi.org/10.1109/bibm.2012.6392613.
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    • Nassif, Houssam, Yirong Wu, David Page, and Elizabeth Burnside. “Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women.” Amia Annu Symp Proc 2012 (2012): 1330–39.
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    • Weiss, J. C., S. Natarajan, P. L. Peissig, C. A. McCarty, and D. Page. “Machine learning for personalized medicine: Predicting primary myocardial infarction from electronic health records.” Ai Magazine 33, no. 4 (January 1, 2012): 33–45. https://doi.org/10.1609/aimag.v33i4.2438.
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    • Dutra, I., H. Nassif, D. Page, J. Shavlik, R. M. Strigel, Y. Wu, M. E. Elezaby, and E. Burnside. “Integrating machine learning and physician knowledge to improve the accuracy of breast biopsy.” Amia Annu Symp Proc 2011 (2011): 349–55.
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    • Woods, Ryan W., Louis Oliphant, Kazuhiko Shinki, David Page, Jude Shavlik, and Elizabeth Burnside. “Validation of results from knowledge discovery: mass density as a predictor of breast cancer.” J Digit Imaging 23, no. 5 (October 2010): 554–61. https://doi.org/10.1007/s10278-009-9235-3.
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    • Hellerstein, L., B. Roseli, E. Bach, S. Ray, and D. Page. “Exploiting product distributions to identify relevant variables of correlation immune functions.” Journal of Machine Learning Research 10 (November 30, 2009): 2375–2411.
    • Burnside, Elizabeth S., Jesse Davis, Jagpreet Chhatwal, Oguzhan Alagoz, Mary J. Lindstrom, Berta M. Geller, Benjamin Littenberg, Katherine A. Shaffer, Charles E. Kahn, and C David Page. “Probabilistic computer model developed from clinical data in national mammography database format to classify mammographic findings.” Radiology 251, no. 3 (June 2009): 663–72. https://doi.org/10.1148/radiol.2513081346.
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    • International Warfarin Pharmacogenetics Consortium, J. A., T. E. Klein, R. B. Altman, N. Eriksson, B. F. Gage, S. E. Kimmel, M. -. T. M. Lee, et al. “Estimation of the warfarin dose with clinical and pharmacogenetic data.” N Engl J Med 360, no. 8 (February 19, 2009): 753–64. https://doi.org/10.1056/NEJMoa0809329.
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    • Herbst, Allen, Sean McIlwain, Joshua J. Schmidt, Judd M. Aiken, C David Page, and Lingjun Li. “Prion disease diagnosis by proteomic profiling.” J Proteome Res 8, no. 2 (February 2009): 1030–36. https://doi.org/10.1021/pr800832s.
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    • Hellerstein, Lisa, Bernard Rosell, Eric Bach, Soumya Ray, and David Page. “Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions.” J. Mach. Learn. Res. 10 (2009): 2374–2411.
    • Struyf, Jan, Seth Dobrin, and David Page. “Combining gene expression, demographic and clinical data in modeling disease: a case study of bipolar disorder and schizophrenia.” Bmc Genomics 9 (November 7, 2008): 531. https://doi.org/10.1186/1471-2164-9-531.
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    • McIlwain, Sean, David Page, Edward L. Huttlin, and Michael R. Sussman. “Matching isotopic distributions from metabolically labeled samples.” Bioinformatics 24, no. 13 (July 1, 2008): i339–47. https://doi.org/10.1093/bioinformatics/btn190.
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    • Santos Costa, V., D. Page, and J. Cussens. “CLP(BN): Constraint logic programming for probabilistic knowledge.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4911 LNAI (March 10, 2008): 156–88. https://doi.org/10.1007/978-3-540-78652-8_6.
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    • Schmidt, Joshua J., Sean McIlwain, David Page, Andrew E. Christie, and Lingjun Li. “Combining MALDI-FTMS and bioinformatics for rapid peptidomic comparisons.” J Proteome Res 7, no. 3 (March 2008): 887–96. https://doi.org/10.1021/pr070390p.
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    • Darnell, Steven J., David Page, and Julie C. Mitchell. “An automated decision-tree approach to predicting protein interaction hot spots.” Proteins 68, no. 4 (September 1, 2007): 813–23. https://doi.org/10.1002/prot.21474.
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    • Severtson, D. J., L. Pape, C. D. Page, J. W. Shavlik, G. N. Phillips, and P. Flatley Brennan. “Biomedical informatics training at the University of Wisconsin-Madison.” Yearb Med Inform, 2007, 149–56.
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    • Page, Carine. “Rocephin--the thin end of the wedge.” S Afr Med J 96, no. 8 (August 2006): 662–63.
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    • Burnside, Elizabeth S., Jesse Davis, Victor Santos Costa, Inês de Castro Dutra, Charles E. Kahn, Jason Fine, and David Page. “Knowledge discovery from structured mammography reports using inductive logic programming.” Amia Annu Symp Proc 2005 (2005): 96–100.
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    • Dutra, I., D. Page, V. S. Costa, J. Shavlik, and M. Waddell. “Toward automatic management of embarrassingly parallel applications.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2790 (December 1, 2004): 509–16.
    • Molla, M., M. Waddell, D. Page, and J. Shavlik. “Using Machine Learning to Design and Interpret Gene-Expression Microarrays.” Ai Magazine 25, no. 1 (March 1, 2004): 23–44.
    • Hardin, Johanna, Michael Waddell, C David Page, Fenghuang Zhan, Bart Barlogie, John Shaughnessy, and John J. Crowley. “Evaluation of multiple models to distinguish closely related forms of disease using DNA microarray data: an application to multiple myeloma.” Stat Appl Genet Mol Biol 3 (2004): Article10. https://doi.org/10.2202/1544-6115.1018.
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    • Bockhorst, Joseph, Mark Craven, David Page, Jude Shavlik, and Jeremy Glasner. “A Bayesian network approach to operon prediction.” Bioinformatics 19, no. 10 (July 1, 2003): 1227–35. https://doi.org/10.1093/bioinformatics/btg147.
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    • Page, David, and Mark Craven. “Biological applications of multi-relational data mining.” Acm Sigkdd Explorations Newsletter 5, no. 1 (July 2003): 69–79. https://doi.org/10.1145/959242.959250.
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    • Cheng, Jie, Christos Hatzis, Hisashi Hayashi, Mark-A Krogel, Shinichi Morishita, David Page, and Jun Sese. “KDD Cup 2001 report.” Acm Sigkdd Explorations Newsletter 3, no. 2 (January 2002): 47–64. https://doi.org/10.1145/507515.507523.
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    • De Raedt, L., C. D. Page, and S. Wrobel. “Special issue on inductive logic programming.” Machine Learning 43, no. 1–2 (April 1, 2001): 5–6.
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    • De Raedt, L., C. D. Page, and S. Wrobel. “Guest editorial.” Machine Learning 43, no. 1–2 (April 1, 2001): 5–6. https://doi.org/10.1023/A:1017394631519.
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    • Craven, M., D. Page, J. Shavlik, J. Bockhorst, and J. Glasner. “A probabilistic learning approach to whole-genome operon prediction.” Proc Int Conf Intell Syst Mol Biol 8 (2000): 116–27.
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    • King, R. D., M. Ouali, A. T. Strong, A. Aly, A. Elmaghraby, M. Kantardzic, and D. Page. “Is it better to combine predictions?” Protein Eng 13, no. 1 (January 2000): 15–19. https://doi.org/10.1093/protein/13.1.15.
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    • Muggleton, Stephen, and David Page. “Guest editors' introduction.” The Journal of Logic Programming 40, no. 2–3 (August 1999): 125–26. https://doi.org/10.1016/s0743-1066(99)00015-1.
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    • Muggleton, Stephen, and David Page. “Guest Editors' Introduction: Inductive Logic Programming.” J. Log. Program. 40 (1999): 125–26. https://doi.org/10.1016/S0743-1066(99)00015-1.
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    • Finn, P., S. Muggleton, D. Page, and A. Srinivasan. “Pharmacophore discovery using the Inductive Logic Programming system PROGOL.” Machine Learning 30, no. 2–3 (January 1, 1998): 241–70. https://doi.org/10.1023/a:1007460424845.
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    • Muggleton, Stephen, and David Page. “Guest Editors' Introduction.” Mach. Learn. 26 (1997): 97–98. https://doi.org/10.1023/A:1007399922151.
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    • Muggleton, Stephen, and David Page. “Special issue on inductive logic programming - Guest Editors' introduction.” Machine Learning 26, no. 2/3 (1997): 97–98. https://doi.org/10.1023/a:1007399922151.
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    • Cohen, William W., and C David Page. “Polynomial learnability and Inductive Logic Programming: Methods and results.” New Generation Computing 13, no. 3–4 (December 1995): 369–409. https://doi.org/10.1007/bf03037231.
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    • Frazier, Michael, and C. David Page. “Prefix grammars: an alternative characterization of the regular languages.” Information Processing Letters 51, no. 2 (July 1994): 67–71. https://doi.org/10.1016/0020-0190(94)00074-3.
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  • Book Sections

    • Kuang, Zhaobin, Yujia Bao, James Thomson, Michael Caldwell, Peggy Peissig, Ron Stewart, Rebecca Willett, and David Page. “A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization.,” 1903:255–67, 2019. https://doi.org/10.1007/978-1-4939-8955-3_15.
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    • Page, C David, and Sriraam Natarajan. “Biomedical Informatics.” In Encyclopedia of Machine Learning and Data Mining, 143–63. Springer US, 2017. https://doi.org/10.1007/978-1-4899-7687-1_30.
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    • Natarajan, S., P. L. Peissig, and D. Page. “Relational learning for sustainable health.” In Studies in Computational Intelligence, 645:245–64, 2016. https://doi.org/10.1007/978-3-319-31858-5_11.
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    • Costa, Vítor Santos, David Page, and James Cussens. “CLP(BN): Constraint Logic Programming for Probabilistic Knowledge.” In Probabilistic Inductive Logic Programming, edited by Luc De Raedt, Paolo Frasconi, Kristian Kersting, and Stephen Muggleton, 4911:156–88. Springer, 2008.
    • Page, David, and David Frisch. “Generalization and learnability: A study of constrained atoms.” In Inductive Logic Programming. Morgan Kaufmann, 1992.

  • Conference Papers

    • Dhami, D. S., S. Yan, G. Kunapuli, D. Page, and S. Natarajan. “Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12721 LNAI:252–57, 2021. https://doi.org/10.1007/978-3-030-77211-6_28.
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    • Zhang, Wei, Peggy Peissig, Zhaobin Kuang, and David Page. “Adverse Drug Reaction Discovery from Electronic Health Records with Deep Neural Networks.” In Proc Acm Conf Health Inference Learn (2020), 2020:30–39, 2020. https://doi.org/10.1145/3368555.3384459.
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    • Zhang, W., H. Wei, B. Sisman, X. L. Dong, C. Faloutsos, and D. Page. “Autoblock: A hands-off blocking framework for entity matching.” In Wsdm 2020  Proceedings of the 13th International Conference on Web Search and Data Mining, 744–52, 2020. https://doi.org/10.1145/3336191.3371813.
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    • Zhang, W., T. K. Panum, S. Jha, P. Chalasani, and D. Page. “CAUSE: Learning granger causality from event sequences using attribution methods.” In 37th International Conference on Machine Learning, Icml 2020, PartF168147-15:11171–81, 2020.
    • Page, David, and Ross Kleiman. “AUCµ: A Performance Metric for Multi-Class Machine Learning Models,” 2019.
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    • Kleiman, R. S., and D. Page. “AUCμ: A performance metric for multi-class machine learning models.” In 36th International Conference on Machine Learning, Icml 2019, 2019-June:5972–83, 2019.
    • Escanilla, Nicholas Sean, Lisa Hellerstein, Ross Kleiman, Zhaobin Kuang, James D. Shull, and David Page. “Recursive Feature Elimination by Sensitivity Testing.” In Proc Int Conf Mach Learn Appl, 2018:40–47, 2018. https://doi.org/10.1109/ICMLA.2018.00014.
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    • Geng, Sinong, Zhaobin Kuang, Jie Liu, Stephen Wright, and David Page. “Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error.” In Uncertain Artif Intell, 2018:156–66, 2018.
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    • Wu, Yirong, Jun Fan, Peggy Peissig, Richard Berg, Ahmad Pahlavan Tafti, Jie Yin, Ming Yuan, David Page, Jennifer Cox, and Elizabeth S. Burnside. “Quantifying predictive capability of electronic health records for the most harmful breast cancer.” In Proc Spie Int Soc Opt Eng, Vol. 10577, 2018. https://doi.org/10.1117/12.2293954.
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    • Geng, Sinong, Zhaobin Kuang, Peggy L. Peissig, and David Page. “Temporal Poisson Square Root Graphical Models.” In Icml, edited by Jennifer G. Dy and Andreas Krause, 80:1700–1709. PMLR, 2018.
    • Giacomelli, I., S. Jha, M. Joye, C. D. Page, and K. Yoon. “Privacy-preserving ridge regression with only linearly-homomorphic encryption.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10892 LNCS:243–61, 2018. https://doi.org/10.1007/978-3-319-93387-0_13.
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    • Wu, Y., E. S. Burnside, J. Cox, J. Fan, M. Yuan, J. Yin, P. Peissig, A. Cobian, D. Page, and M. Craven. “Breast Cancer Risk Prediction Using Electronic Health Records.” In Proceedings  2017 Ieee International Conference on Healthcare Informatics, Ichi 2017, 224–28, 2017. https://doi.org/10.1109/ICHI.2017.62.
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    • Kuang, Zhaobin, Peggy Peissig, Vítor Santos Costa, Richard Maclin, and David Page. “Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data.” In Kdd, 2017:1537–46, 2017. https://doi.org/10.1145/3097983.3097998.
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    • Tafti, A. P., E. Behravesh, M. Assefi, E. Larose, J. Badger, J. Mayer, A. Doan, D. Page, and P. Peissig. “BigNN: An open-source big data toolkit focused on biomedical sentence classification.” In Proceedings  2017 Ieee International Conference on Big Data, Big Data 2017, 2018-January:3888–96, 2017. https://doi.org/10.1109/BigData.2017.8258394.
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    • Dhami, Devendra Singh, Ameet Soni, David Page, and Sriraam Natarajan. “Identifying Parkinson's Patients: A Functional Gradient Boosting Approach.” In Artif Intell Med Conf Artif Intell Med (2005 ), 10259:332–37, 2017. https://doi.org/10.1007/978-3-319-59758-4_39.
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    • Badger, Jonathan C., Eric R. LaRose, Ross Kleiman, Richard L. Berg, James G. Linneman, Richard Hansen, David Page, and Peggy L. Peissig. “SCCS for Detection of Differences in Brand and Generic Adverse Drug Events.” In Cri. AMIA, 2017.
    • Bao, Yujia, Zhaobin Kuang, Peggy L. Peissig, David Page, and Rebecca Willett. “Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data.” In Mlhc, edited by Finale Doshi-Velez, Jim Fackler, David C. Kale, Rajesh Ranganath, Byron C. Wallace, and Jenna Wiens, 68:177–90. PMLR, 2017.
    • Kuang, Z., S. Geng, and D. Page. “A screening rule for ℓ1-regularized ising model estimation.” In Advances in Neural Information Processing Systems, 2017-December:721–32, 2017.
    • Kuang, Zhaobin, James Thomson, Michael Caldwell, Peggy Peissig, Ron Stewart, and David Page. “Computational Drug Repositioning Using Continuous Self-Controlled Case Series.” In Kdd, 2016:491–500, 2016. https://doi.org/10.1145/2939672.2939715.
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    • Kuang, Zhaobin, James Thomson, Michael Caldwell, Peggy Peissig, Ron Stewart, and David Page. “Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data.” In Ijcai (U S), 2016:2521–28, 2016.
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    • Boyd, K., E. Lantz, and D. Page. “Differential privacy for classifier evaluation.” In Aisec 2015  Proceedings of the 8th Acm Workshop on Artificial Intelligence and Security, Co Located With Ccs 2015, 15–24, 2015. https://doi.org/10.1145/2808769.2808775.
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    • Lantz, E., K. Boyd, and D. Page. “Subsampled exponential mechanism: Differential privacy in large output spaces.” In Aisec 2015  Proceedings of the 8th Acm Workshop on Artificial Intelligence and Security, Co Located With Ccs 2015, 25–34, 2015. https://doi.org/10.1145/2808769.2808776.
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    • Weiss, J. C., S. Natarajan, and C. D. Page. “Learning to reject sequential importance steps for continuous-time Bayesian networks.” In Proceedings of the National Conference on Artificial Intelligence, 5:3628–34, 2015.
    • Odom, Phillip, Vishal Bangera, Tushar Khot, David Page, and Sriraam Natarajan. “Extracting Adverse Drug Events from Text using Human Advice.” In Artif Intell Med Conf Artif Intell Med (2005 ), 2015:195–204, 2015. https://doi.org/10.1007/978-3-319-19551-3_26.
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    • Wu, Y., J. Liu, A. Munoz Del Rio, D. C. Page, O. Alagoz, P. Peissig, A. A. Onitilo, and E. S. Burnside. “Developing a clinical utility framework to evaluate prediction models in radiogenomics.” In Progress in Biomedical Optics and Imaging  Proceedings of Spie, Vol. 9416, 2015. https://doi.org/10.1117/12.2081954.
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    • Zeng, Q., J. M. Patel, and D. Page. “QuickFOIL: Scalable inductive logic programming.” In Proceedings of the Vldb Endowment, 8:197–208, 2014. https://doi.org/10.14778/2735508.2735510.
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    • Fredrikson, M., E. Lantz, S. Jha, S. Lin, D. Page, and T. Ristenpart. “Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing.” In Proceedings of the 23rd Usenix Security Symposium, 17–32, 2014.
    • Kuusisto, Finn, Vitor Santos Costa, Houssam Nassif, Elizabeth Burnside, David Page, and Jude Shavlik. “Support Vector Machines for Differential Prediction.” In Mach Learn Knowl Discov Databases, 8725:50–65, 2014. https://doi.org/10.1007/978-3-662-44851-9_4.
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    • Liu, J., C. Zhang, E. Burnside, and D. Page. “Multiple testing under dependence via semiparametric graphical models.” In 31st International Conference on Machine Learning, Icml 2014, 3:2601–13, 2014.
    • Liu, Jie, Chunming Zhang, Elizabeth Burnside, and David Page. “Learning Heterogeneous Hidden Markov Random Fields.” In Jmlr Workshop Conf Proc, 33:576–84, 2014.
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    • Liu, Jie, and David Page. “Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models.” In Adv Neural Inf Process Syst, 2013:1232–40, 2013.
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    • Boyd, K., K. H. Eng, and C. D. Page. “Area under the precision-recall curve: Point estimates and confidence intervals.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8190 LNAI:451–66, 2013. https://doi.org/10.1007/978-3-642-40994-3_29.
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    • Weiss, J. C., and D. Page. “Forest-based point process for event prediction from electronic health records.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8190 LNAI:547–62, 2013. https://doi.org/10.1007/978-3-642-40994-3_35.
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    • Davis, J., V. S. Costa, P. Peissig, M. Caldwell, and D. Page. “A preliminary investigation into predictive models for adverse drug events.” In Aaai Workshop  Technical Report, WS-13-09:8–13, 2013.
    • Nassif, Houssam, Finn Kuusisto, Elizabeth S. Burnside, David Page, Jude Shavlik, and Vítor Santos Costa. “Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling.” In Mach Learn Knowl Discov Databases, 8190:595–611, 2013. https://doi.org/10.1007/978-3-642-40994-3_38.
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    • Weiss, J. C., S. Natarajan, and C. D. Page. “Learning when to reject an importance sample.” In Aaai Workshop  Technical Report, WS-13-17:143–45, 2013.
    • Weiss, J. C., S. Natarajan, and D. Page. “Multiplicative forests for continuous-time processes.” In Advances in Neural Information Processing Systems, 1:458–66, 2012.
    • Liu, J., H. Vidaillet, E. Burnside, and D. Page. “A collective ranking method for genome-wide association studies.” In 2012 Acm Conference on Bioinformatics, Computational Biology and Biomedicine, Bcb 2012, 313–20, 2012. https://doi.org/10.1145/2382936.2382976.
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    • Page, D., V. S. Costa, S. Natarajan, A. Barnard, P. Peissig, and M. Caldwell. “Identifying adverse drug events by relational learning.” In Proceedings of the National Conference on Artificial Intelligence, 2:1599–1605, 2012.
    • Boyd, K., V. S. Costa, J. Davis, and C. D. Page. “Unachievable region in precision-recall space and its effect on empirical evaluation.” In Proceedings of the 29th International Conference on Machine Learning, Icml 2012, 1:639–46, 2012.
    • Nassif, H., V. Santos Costa, E. S. Burnside, and D. Page. “Relational differential prediction.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7523 LNAI:617–32, 2012. https://doi.org/10.1007/978-3-642-33460-3_45.
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    • Davis, Jesse, Vítor Santos Costa, Peggy Peissig, Michael Caldwell, Elizabeth Berg, and David Page. “Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events.” In Proc Int Conf Mach Learn, 2012:1287–94, 2012.
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    • Liu, Jie, Chunming Zhang, Catherine A. McCarty, Peggy L. Peissig, Elizabeth S. Burnside, and David Page. “Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies.” In Uai, edited by Nando de Freitas and Kevin P. Murphy, 511–22. AUAI Press, 2012.
    • Liu, Jie, Peggy Peissig, Chunming Zhang, Elizabeth Burnside, Catherine McCarty, and David Page. “High-Dimensional Structured Feature Screening Using Binary Markov Random Fields.” In Jmlr Workshop Conf Proc, 22:712–21, 2012.
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    • Liu, Jie, Peggy Peissig, Chunming Zhang, Elizabeth Burnside, Catherine McCarty, and David Page. “Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies.” In Uncertain Artif Intell, 2012:511–22, 2012.
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    • Weiss, Jeremy C., David Page, Peggy L. Peissig, Sriraam Natarajan, and Catherine McCarty. “Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records.” In Proc Innov Appl Artif Intell Conf, 2012:2341–47, 2012.
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    • Karnik, Shreyas, Sin Lam Tan, Bess Berg, Ingrid Glurich, Jinfeng Zhang, Humberto J. Vidaillet, C David Page, and Rajesh Chowdhary. “Predicting atrial fibrillation and flutter using electronic health records.” In Annu Int Conf Ieee Eng Med Biol Soc, 2012:5562–65, 2012. https://doi.org/10.1109/EMBC.2012.6347254.
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    • Walker, T., G. Kunapuli, N. Larsen, D. Page, and J. Shavlik. “Integrating knowledge capture and supervised learning through a human-computer interface.” In Kcap 2011  Proceedings of the 2011 Knowledge Capture Conference, 89–96, 2011. https://doi.org/10.1145/1999676.1999693.
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    • Walker, T., C. O’Reilly, G. Kunapuli, S. Natarajan, R. MacLin, D. Page, and J. Shavlik. “Automating the ILP setup task: Converting user advice about specific examples into general background knowledge.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6489 LNAI:253–68, 2011. https://doi.org/10.1007/978-3-642-21295-6_28.
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    • Nassif, H., D. Page, M. Ayvaci, J. Shavlik, and E. S. Burnside. “Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming.” In Ihi’10  Proceedings of the 1st Acm International Health Informatics Symposium, 76–82, 2010. https://doi.org/10.1145/1882992.1883005.
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    • Nassif, Houssam, Hassan Al-Ali, Sawsan Khuri, Walid Keirouz, and David Page. “An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge.” In Inductive Log Program, 5989:149–65, 2010. https://doi.org/10.1007/978-3-642-13840-9_14.
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    • Nassif, Houssam, Ryan Woods, Elizabeth Burnside, Mehmet Ayvaci, Jude Shavlik, and David Page. “Information Extraction for Clinical Data Mining: A Mammography Case Study.” In Proc Ieee Int Conf Data Min, 37–42, 2009. https://doi.org/10.1109/icdmw.2009.63.
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    • Davis, J., I. Ong, J. Struyf, E. Burnside, D. Page, and V. S. Costa. “Change of representation for statistical relational learning.” In Ijcai International Joint Conference on Artificial Intelligence, 2719–26, 2007.
    • Lantz, E., S. Ray, and D. Page. “Learning Bayesian network structure from correlation-immune data.” In Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, Uai 2007, 235–42, 2007.
    • Davis, J., V. S. Costa, S. Ray, and D. Page. “An integrated approach to feature invention and model construction for drug activity prediction.” In Acm International Conference Proceeding Series, 227:217–24, 2007. https://doi.org/10.1145/1273496.1273524.
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    • McIlwain, Sean, David Page, Edward L. Huttlin, and Michael R. Sussman. “Using dynamic programming to create isotopic distribution maps from mass spectra.” In Bioinformatics, 23:i328–36, 2007. https://doi.org/10.1093/bioinformatics/btm198.
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    • Lantz, Eric, Soumya Ray, and David Page. “Learning Bayesian Network Structure from Correlation-Immune Data.” In Uai, edited by Ronald Parr and Linda C van der Gaag, 235–42. AUAI Press, 2007.
    • Ong, I. M., S. E. Topper, D. Page, and V. S. Costa. “Inferring regulatory networks from time series expression data and relational data via inductive logic programming.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4455 LNAI:366–78, 2007. https://doi.org/10.1007/978-3-540-73847-3_34.
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    • Paes, A., F. Železný, G. Zaverucha, D. Page, and A. Srinivasan. “ILP through propositionalization and stochastic k-term DNF learning.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4455 LNAI:379–93, 2007. https://doi.org/10.1007/978-3-540-73847-3_35.
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    • Srinivasan, A., D. Page, R. Camacho, and R. King. “Quantitative pharmacophore models with inductive logic programming.” In Machine Learning, 64:65–90, 2006. https://doi.org/10.1007/s10994-006-8262-2.
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    • Železný, F., A. Srinivasan, and C. D. Page. “Randomised restarted search in ILP.” In Machine Learning, 64:183–208, 2006. https://doi.org/10.1007/s10994-006-7733-9.
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    • Page, David, and Irene M. Ong. “Experimental design of time series data for learning from dynamic Bayesian networks.” In Pac Symp Biocomput, 267–78, 2006.
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    • Struyf, J., J. Davis, and D. Page. “An efficient approximation to lookahead in relational learners.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4212 LNAI:775–82, 2006. https://doi.org/10.1007/11871842_79.
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    • Davis, J., E. Burnside, I. De Castro Dutra, D. Page, and V. Santos Costa. “An integrated approach to learning Bayesian networks of rules.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3720 LNAI:84–95, 2005. https://doi.org/10.1007/11564096_13.
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    • Davis, J., E. Burnside, I. Dutra, D. Page, R. Ramakrishnan, V. S. Costa, and J. Shavlik. “View learning for statistical relational learning: With an application to mammography.” In Ijcai International Joint Conference on Artificial Intelligence, 677–83, 2005.
    • Ong, I. M., I. De Castro Dutra, D. Page, and V. S. Costa. “Mode directed path finding.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3720 LNAI:673–81, 2005. https://doi.org/10.1007/11564096_68.
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    • Rosell, B., L. Hellerstein, S. Ray, and D. Page. “Why skewing works: Learning difficult boolean functions with greedy tree learners.” In Icml 2005  Proceedings of the 22nd International Conference on Machine Learning, 729–36, 2005.
    • Waddell, M., D. Page, and J. Shaughnessy. “Predicting cancer susceptibility from single-nucleotide polymorphism data: A case study in multiple myeloma.” In Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 21–28, 2005. https://doi.org/10.1145/1134030.1134035.
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    • Page, David, Hendrik Blockeel, and Ashwin Srinivasan. “Multi-instance tree learning,” 57–64, 2005. https://doi.org/10.1145/1102351.1102359.
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    • Bravo, H. C., D. Page, R. Ramakrishnan, J. Shavlik, and V. S. Costa. “A framework for set-oriented computation in inductive logic programming and its application in generalizing inverse entailment.” In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3625:69–86, 2005. https://doi.org/10.1007/11536314_5.
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    • Ray, S., and D. Page. “Generalized skewing for functions with continuous and nominal attributes.” In Icml 2005  Proceedings of the 22nd International Conference on Machine Learning, 705–12, 2005. https://doi.org/10.1145/1102351.1102440.
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    • Rosell, Bernard, Lisa Hellerstein, Soumya Ray, and David Page. “Why skewing works: learning difficult Boolean functions with greedy tree learners.” In Icml, edited by Luc De Raedt and Stefan Wrobel, 119:728–35. ACM, 2005.
    • Ray, S., and D. Page. “Sequential skewing: An improved skewing algorithm.” In Proceedings, Twenty First International Conference on Machine Learning, Icml 2004, 679–86, 2004.
    • Page, D., and A. Srinivasan. “ILP: A short look back and a longer look forward.” In Journal of Machine Learning Research, 4:415–30, 2004. https://doi.org/10.1162/153244304773936009.
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    • Železný, F., A. Srinivasan, and D. Page. “A Monte Carlo study of randomised restarted search in ILP.” In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3194:341–58, 2004. https://doi.org/10.1007/978-3-540-30109-7_25.
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    • Page, D., and S. Ray. “Skewing: An efficient alternative to lookahead for decision tree induction.” In Ijcai International Joint Conference on Artificial Intelligence, 601–7, 2003.
    • Costa, Vítor Santos, David Page, Maleeha Qazi, and James Cussens. “CLP(BN): Constraint Logic Programming for Probabilistic Knowledge.” In Uai, edited by Christopher Meek and Uffe Kjærulff, 517–24. Morgan Kaufmann, 2003.
    • De Castro Dutra, I., D. Page, V. S. Costa, and J. Shavlik. “An empirical evaluation of bagging in inductive logic programming.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2583:48–65, 2003. https://doi.org/10.1007/3-540-36468-4_4.
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    • Graham, J., C. D. Page, and A. Kamal. “Accelerating the drug design process through parallel inductive logic programming data mining.” In Proceedings of the 2003 Ieee Bioinformatics Conference, Csb 2003, 400–402, 2003. https://doi.org/10.1109/CSB.2003.1227345.
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    • Železný, F., A. Srinivasan, and D. Page. “Lattice-search runtime distributions may be heavy-tailed.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2583:333–45, 2003. https://doi.org/10.1007/3-540-36468-4_22.
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    • Kama, A. H., J. H. Graham, and C. D. Page. “Advancing drug discovery through parallel inductive logic programming.” In Parallel and Distributed Computing Systems, edited by W. W. Smari and M. Guizani, 367–72. INTERNATIONAL SOCIETY COMPUTER S & THEIR APPLICATIONS (ISCA), 2002.
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    • Ong, Irene M., Jeremy D. Glasner, and David Page. “Modelling regulatory pathways in E. coli from time series expression profiles.” In Bioinformatics, 18 Suppl 1:S241–48, 2002. https://doi.org/10.1093/bioinformatics/18.suppl_1.s241.
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    • Page, D. “The role of declarative languages in mining biological databases.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2562:1, 2002.
    • Ray, Soumya, and David Page. “Multiple Instance Regression.” In Icml, edited by Carla E. Brodley and Andrea Pohoreckyj Danyluk, 425–32. Morgan Kaufmann, 2001.
    • Kamal, A. H., J. H. Graham, and C. D. Page. “An Approach to Parallel Data Mining for Pharmacophore Discovery.” In 10th Golden West International Conference on Intelligent Systems 2001, Icis 2001, 100–103, 2001.
    • Craven, Mark, David Page, Jude W. Shavlik, Joseph Bockhorst, and Jeremy D. Glasner. “Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes.” In Icml, edited by Pat Langley, 199–206. Morgan Kaufmann, 2000.
    • Graham, J., C. D. Page, and A. Wild. “Parallel data mining for pharmacophore discovery.” In Proceedings of the Ieee International Conference on Systems, Man and Cybernetics, 3:1894–99, 2000. https://doi.org/10.1109/ICSMC.2000.886389.
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    • Page, D. “Ilp: Just do it.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1866:3–18, 2000. https://doi.org/10.1007/3-540-44960-4_1.
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    • Page, D. “ILP: Just do it.” In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 1861:25–40, 2000. https://doi.org/10.1007/3-540-44957-4_2.
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    • Spatola, A. F., C. D. Page, D. M. Vogel, Y. Crozet, and S. Blondelle. “Can machine learning and combinatorial chemistry coexist? An antimicrobial peptide case study.” In Peptides for the New Millennium, edited by G. B. Fields, J. P. Tam, and G. Barany, 738–39. SPRINGER, 2000.
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    • Muggleton, S., D. Page, and A. Srinivasan. “An initial experiment into stereochemistry-based drug design using inductive logic programming.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1314:25–40, 1997. https://doi.org/10.1007/3-540-63494-0_46.
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    • Muggleton, Stephen, and David Page. “A Learnability Model for Universal Representations and Its Application to Top-down Induction of Decision Trees.” In Machine Intelligence 15, edited by Koichi Furukawa, Donald Michie, and Stephen Muggleton, 248–67. Oxford University Press, 1995.
    • FRAZIER, M., and C. D. PAGE. “LEARNABILITY IN INDUCTIVE LOGIC PROGRAMMING - SOME BASIC RESULTS AND TECHNIQUES.” In Proceedings of the Eleventh National Conference on Artificial Intelligence, 93–98. M I T PRESS, 1993.
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    • PAGE, C. D., and A. M. FRISCH. “GENERALIZING ATOMS IN CONSTRAINT LOGIC.” In Principles of Knowledge Representation and Reasoning, edited by J. ALLEN, R. FIKES, and E. SANDEWALL, 429–40. MORGAN KAUFMANN PUB INC, 1991.
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    • PAGE, C. D., and A. M. FRISCH. “LEARNING CONSTRAINED ATOMS.” In Machine Learning, edited by L. A. Birnbaum and G. C. Collins, 427–31. MORGAN KAUFMANN PUB INC, 1991.
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    • FRISCH, A. M., and C. D. PAGE. “GENERALIZATION WITH TAXONOMIC INFORMATION.” In Proceedings : Eighth National Conference on Artificial Intelligence, Vols 1 and 2, 755–61. AMER ASSOC ARTIFICIAL INTELLIGENCE, 1990.
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  • Theses and Dissertations

    • Page, Charles. “Anti-Unification in Constraint Logics: Foundations and Applications to Learnability in First-Order Logic, to Speed-Up Learning, and to Deduction,” 1993.
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• Teaching & Mentoring
• 

  Recent Courses

  • BIOSTAT 823: Statistical Program for Big Data 2023
  • BIOSTAT 824: Case Studies in Biomedical Data Science 2023
  • BIOSTAT 823: Statistical Program for Big Data 2022
  • BIOSTAT 824: Case Studies in Biomedical Data Science 2022
  • BIOSTAT 824: Case Studies in Biomedical Data Science 2021
• 

  Teaching Activities

  • Most Recent Teaching at the University of Wisconsin-Madison:
    
    BMI/CS 576: Bioinformatics
    CS 731: Advanced Methods in Artificial Intelligence
    CS 760: Machine Learning
    CS 838: Statistical Relational Learning
    Med/BMI 918: Health Informatics
    
    
    
• Scholarly, Clinical, & Service Activities
• 

  Academic & Administrative Activities

  • Author of UW Computational Biology Web Page in the CS Dept

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