University of Pittsburgh

Artificial Intelligence in Population Health Lab

Publications

  1. Ji Y, Gao Y, Bao R, Li Q, Liu D, Sun Y, and Ye Y*. Prediction of COVID-19 Patientsʼ Emergency Room Revisit using Multi-Source Transfer Learning. (Accepted by IEEE ICHI 2023) Preprint available at https://arxiv.org/abs/2306.17257
  2. Gao Y, Mazurek JM, Li Y, Blackley D, Weissman DN, Burton SV, Amin W, Landsittel D, Becich MJ, Ye Y*. Industry, occupation, and exposure history of mesothelioma patients in the U.S. National Mesothelioma Virtual Bank, 2006-2022. Environmental Research. 2023.
    https://doi.org/10.1016/j.envres.2022.115085
  3. Hartman D, Douget JE Le, Ye Y, Li Y, Sin-Chan P, Pronier E, Becich M. Application of deep learning models on whole slide images uncover new histological markers related to high-risk malignant pleural mesothelioma. Journal of Clinical Oncology 40 (16_suppl), e13580-e13580, 2022.
    https://ascopubs.org/doi/abs/10.1200/JCO.2022.40.16_suppl.e13580
  4. Ye Y, Barapatre S, Davis MK, Elliston KO, Davatzikos C, Fedorov A et al. Open-source Software Sustainability Models: Initial White Paper From the Informatics Technology for Cancer Research Sustainability and Industry Partnership Working Group. Journal of medical Internet research 23 (12), e20028, 2021.
    https://www.jmir.org/2021/12/e20028/
  5. Jackson BR, Ye Y, Crawford JM, Becich MJ, Roy S et al. The Ethics of Artificial Intelligence in Pathology and Laboratory Medicine: Principles and Practice. Academic Pathology 8, 2374289521990784, 2021.
    https://journals.sagepub.com/doi/pdf/10.1177/2374289521990784
  6. Bernstam EV, Shireman PK, Meric‐Bernstam F, Zozus M, Jiang X, Bradley BB, Ashley KW, Susanne S, Shyam V, Ye Y et al. Artificial Intelligence in Clinical and Translational Science: Successes, Challenges and Opportunities. Clinical and Translational Science, 2021.
    https://ascpt.onlinelibrary.wiley.com/doi/full/10.1111/cts.13175
  7. Aronis JM, Ferraro JP, Gesteland PH, Tsui F, Ye Y et al. A Bayesian approach for detecting a disease that is not being modeled. PLOS ONE 15 (2), e0229658, 2020.
    https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0229658
  8. Ye Y, Wagner MM, Cooper GF, et al. Bayesian network transfer learning to improve re-usability of computable biomedical knowledge for public health. Poster abstract accepted by Mobilizing Computable Biomedical Knowledge 2nd Annual Meeting in National Institutes of Health-Bethesda, MD, July 18-19, 2019.
  9. Tsui F, Ye Y, Ruiz V, Cooper GF, Wagner MM. Automated influenza case detection for public health surveillance and clinical diagnosis using dynamic influenza prevalence method. Journal of Public Health, Volume 40, Issue 4, 878–885, 2018.
    https://academic.oup.com/jpubhealth/article/40/4/878/4559110
  10. Aronis JM, Millett NE, Wagner MM, Tsui F, Ye Y et al. A Bayesian System to Detect and Characterize Overlapping Outbreaks. Journal of Biomedical Informatics 73, 171-181, 2017.
    https://www.sciencedirect.com/science/article/pii/S153204641730182X
  11. Tsui F, Shi L, Ruiz V, Barda A, Ye Y, et al. SMM4H: 2nd Social Media Mining for Health Applications Workshop & Shared Task, Washington, DC, Nov. 4, 2017.
    http://ceur-ws.org/Vol-1996/paper12.pdf
  12. Ye Y, Wagner MM, Cooper GF, Ferraro JP, Su H, Gesteland PH, Haug PJ, Millett NE, Aronis JM, Nowalk AJ, Ruiz VM. A study of the transferability of influenza case detection systems between two large healthcare systems. PLoS One. 2017 Apr 5;12(4): e0174970.
    https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0229658
  13. Ferraro JP, Ye Y, Gesteland PH, Haug PJ, Tsui F, Cooper GF, Van Bree R, Ginter T, Nowalk AJ, Wagner MM. The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance. Applied Clinical Informatics. 2017 Feb;8(02):560-80.
    https://www.thieme-connect.com/products/ejournals/html/10.4338/ACI-2016-12-RA-0211
  14. Pineda AL, Ye Y, Visweswaran S, Cooper GF, Wagner MM, Tsui FR. Comparison of machine learning classifiers for influenza detection from emergency department free-text reports. Journal of biomedical informatics 58, 60-69, 2015.
    https://www.sciencedirect.com/science/article/pii/S1532046415001872
  15. Rexit R, Tsui FR, Espino J, Chrysanthis PK, Wesaratchakit S, Ye Y. An analytics appliance for identifying (near) optimal over-the-counter medicine products as health indicators for influenza surveillance. Information Systems 48, 151-163, 2015.
    https://www.sciencedirect.com/science/article/abs/pii/S0306437914000921
  16. Ye Y, Tsui F, Wagner M, Espino JU, Li Q. Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers. Journal of the American Medical Informatics Association. 2014 Jan 9;21(5):815-23.
    https://academic.oup.com/jamia/article/21/5/815/758360
  17. Tang L, Lyles RH, Ye Y, Lo Y, King CC. Extended matrix and inverse matrix methods utilizing internal validation data when both disease and exposure status are misclassified. Epidemiologic methods. 2013 Sep 1;2(1):49-66. *Awarded Best Statistical Science Theoretical Paper from Centers for Disease Control and Prevention in 2014.
    https://www.degruyter.com/document/doi/10.1515/em-2013-0008/html
  18. Lu S, Ye Y, Tsui R, Su H, Rexit R, Wesaratchakit S, Liu X, Hwa R. Domain ontology-based feature reduction for high dimensional drug data and its application to 30-day heart failure readmission prediction. 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, 478-484, 2013.
    https://ieeexplore.ieee.org/abstract/document/6680015
  19. Ruhsary R, Tsui F, Espino J, Wesaratchakit S, Ye Y, et al. Using a distributed search engine to identify optimal product sets for use in an outbreak detection system. In Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom), 2012 8th International Conference on, pp.560-566. IEEE, 2012.
    https://ieeexplore.ieee.org/abstract/document/6450952

Preprint

  1. Bao R., Sun Y., Gao Y., Wang J., Yang Q., Chen H., Mao Z., Ye Y*. A Survey of Heterogeneous Transfer Learning. https://arxiv.org/abs/2310.08459
  2. Barapatre S, Amin W*, Gao Y, Li Y, Becich MJ, Ye Y*. Multiple institutions’ research findings using the National Mesothelioma Virtual Bank. F1000Research. 2022 Nov 18;11(1343):1343. https://f1000research.com/articles/11-1343
  3. Ye Y, Gu A. Deep Transfer Learning for Infectious Disease Case Detection Using Electronic Medical Records.
    https://arxiv.org/abs/2103.06710

GitHub