Ph.D. student @ AI for Health Institute

Hi! my name is Ziqi Xu. I am a third year PhD student in the McKelvey School of Engineering for Computer Science & Engineering at the Washington University of St. Louis, fortunated to be supervised by Prof. Chenyang Lu. My current research interest is machine learning for healthcare.

Research

Current projects

Multi-task Recovery Prediction After Lumber Spine Surgery
  • Predict three clinical outcomes with pre-operation data collected by medical records, EMA questionaires, wearable devices, and DSEM features for patients underwent lumber spine surgery
  • Investigate task relationships, clinical feature importance by average gain to assist clinicians with states-of-art models
    • Uncertainty-aware Multimodal Prediction for Persistent Postsurgical Pain
  • Employed Evidential Deep Learning and Multimodal fusion to predict the persistent postsurgical pain that achieves 0.75 in AUROC with better calibration brier score of 0.2
  • Quantify uncertainty and conduct quantitive and qualitive evaluate over reliability of the measurements to help with clinical-decision making
    • Unsupervised clustering for Heterogeneous Endophenotypes for Chiari patients
  • Combined data-driven methods with clinical knowledge of pre-selected groups for feature selection
  • Demonstrated the stability and reproducibility of the results with Ajusted Rand Score over 0.6

    • Publications

  • Xu Z., Zhang, J., .,…& Lu, C. 2025. Incorporating Uncertainty in Predictive Models Using Mobile Sensing and Clinical Data: A Case Study on Persistent Post-Surgical Pain. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 9, 2, Article 58.

  • Xu, Z., Zhang J., Greenberg J.K., Frumkin, M., Javeed, S., Zhang, J., Benedict, B., Botterbush, K., Rodebaugh T., Ray W., and Lu, C. 2024. Predicting Multi-dimensional Surgical Outcomes with Multi-modal Mobile Sensing: A Case Study with Patients Undergoing Lumbar Spine Surgery. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 8, 2, Article 0 (June 2024), 30 pages. https://doi.org/10.1145/3659628

  • Gupta, S.*, Xu, Z.*, Greenberg, J. K.,…Lu, C. Using Artificial Intelligence to Identify Three Presenting Phenotypes of Chiari Type-1 Malformation and Syringomyelia. Neurosurgery November 18, 2024.

  • Greenberg, J. K.*, Frumkin, M.*, Xu, Z.*, Zhang, J., … Ray, W. Z. (2024). Preoperative Mobile Health Data Improve Predictions of Recovery From Lumbar Spine Surgery. Neurosurgery, 10-1227.

  • Xue, B., Said, A. S., Xu, Z., Liu, H., Shah, N., Yang, H., ... Lu, C. (2023, August). Assisting clinical decisions for scarcely available treatment via disentangled latent representation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5360-5371).

  • Najjuuko C, Brathwaite R, Xu, Z., …Lu, C, Ssewamala FM. Using machine learning to predict poor adherence to antiretroviral therapy among adolescents living with HIV in low-resource settings. AIDS. 2025 Feb 25.

  • Yakdan, S., Botterbush, K., Xu, Z., Lu, C., Ray, W. Z., Greenberg, J. K. (2023, September). Machine learning and lumbar spondylolisthesis. In Seminars in Spine Surgery (Vol. 35, No. 3, p. 101048). WB Saunders.

  • N. Shah, B. Xue, Z. Xu, H. Yang, E. Marwali, H. Dalton, P.P.R. Payne, C. Lu, A.S. Said; ISARIC Clinical Characterisation Group, Validation of Extracorporeal Membrane Oxygenation Mortality Prediction and Severity of Illness Scores in an International COVID-19 Cohort, Artificial Organs, 2023.

  • J.K. Greenberg, S. Javeed, J.K Zhang, B. Benedict, M. Frumkin, Z. Xu, J. Zhang, T. Rodebaugh, C. Lu, M. Steinmetz, Z. Ghogawala, M. Bydon, W.Z. Ray, Current and Future Applications of Mobile Health Technology for Evaluating Spine Surgery Patients: A Review, Journal of Neurosurgery: Spine, 2023.

    • Curriculum Vitae

    Download my CV here

    Get in touch with me!

    Email: ziqixu@wustl.edu

    Office: McKelvey Hall, 1040 One Brookings Drive, St. Louis, MO 63130