Xiaofeng Wang Research Program

Research

Overview

The Clinical Machine Learning Lab is interested in developing novel machine learning and artificial intelligence methods and using these techniques to advance health care. Our current on-going research projects include:

• MR Fingerprinting in Epilepsy

Epilepsy affects 65 million people worldwide; approximately 30% of them do not respond to medications but can be cured by surgery. Conventional MRI can be limited in detecting subtle epileptic lesions or identifying active/epileptic lesions among widespread, multifocal lesions. We use the novel Magnetic resonance fingerprinting (MRF) technique to generate high-resolution quantitative tissue property maps (T1 and T2 maps). We are developing MRF-based radiomic machine learning models to actually predict epilepsy outcomes.
Funding: NIH NINDS

• MR Fingerprinting in Brain Tumors

This project focuses on the development and clinical translation of an MR Fingerprinting based quantitative imaging and analysis platform, with a specific application for glioblastoma infiltration prediction for targeted surgery or radiation therapy.
Funding: NIH NCI

• RWD and RWE Research in COVID-19

COVID-19 has created an unprecedented challenge to public health and security, resulting in an urgent need for the rapid development and application of innovative methods. Real-world data (RWD) and real-world evidence (RWE) offer the ability to better understand (and use) COVID-19 therapeutics and diagnostics. We are conducting multiple research projects on COVID-19 outpatient treatments, COVID ICU treatments, and long COVID conditions using Cleveland Clinic real-world electronic health record data.

• Clinical NLP

Natural language processing (NLP) can extract coded data from clinical text, making historical clinical unstructured information available for analytics and modeling. With the recent algorithm improvements, NLP is more powerful and accessible than ever before in clinical informatics. Our group is developing clinical NLP pipelines using modern transfer learning and semi-supervised learning techniques.

Announcements

30 June, 2023

The Department of Quantitative Health Sciences (QHS) is pleased to announce that Xiaofeng Wang, PhD, has been elected as a 2023 Fellow of the American Statistical Association (ASA).  

The ASA is the world’s largest community of statisticians and is the second-oldest continuously operating professional association in the country. The designation of ASA Fellow has been a significant honor for over 100 years. Under ASA bylaws, only one-third of one percent of the total association membership may be elected as fellows each year. Fellows are evaluated on their contributions to the advancement of statistical science, the impact of their published works, their position with their employer and activities with the ASA and other professional societies. “To be honored, nominees must have an established reputation in the profession and have made outstanding contributions to statistical science,” the ASA says. 

Dr. Wang's citation by the Committee on Fellows reads, “for outstanding scholarly contributions to the methods and applications in the areas of quantitative imaging analysis, clinical data modeling, and approximate Bayesian inference; for significant contributions to collaborative research in medicine”. 

Selected Publications

Peer Reviewed Books

Wang, X.F., Yue, Y.R., and Faraway, J. (2018) Bayesian Regression Modeling and Fast Computation: With Application using R-INLA. Chapman & Hall/CRC Computer Science and Data Analysis Series.

Peer Reviewed Article

(over 190 peer-reviewed papers)

Wang, X.F., Pennello, G., DeSouza, N.M., Huang, E.P., Buckler, A.J., Barnhart, H.X., Delfino, J.G., Raunig, D.L., Wang, L. †, Guimaraes, A.R., Hall, T.J., Obuchowski, N.A. (2023). Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics. Academic Radiology, 30(2): 215-229.

Wang, X.F., Zein, J., Ji, X. and Lin, D.-Y. (2023). Impact of Vaccination, Prior Infection and Therapy on Omicron Infection and Mortality. Journal of Infectious Diseases, doi: 10.1093/infdis/jiac460.

Ren, R., Fang, K., Zhang, Q., and Wang. X.F. (2023) Multivariate Functional Data Clustering Using Adaptive Density Peak Detection. Statistics in Medicine, 1-18, doi:10.1002/sim.9687.

Sun, Y., Li, J., Xu, Y., Zhang, T., & Wang, X. (2023). Deep learning versus conventional methods for missing data imputation: A review and comparative study. Expert Systems with Applications, 120201.

Wang, Y., Yan, G., Wang, X.F., Li, S., Peng, L., Tudorascu, D.L., and Zhang, T. (2023). A Variational Bayesian Approach to Identifying Whole-Brain Directional Networks with fMRI Data. The Annals of Applied Statistics. 17(1), 518-538.

Sun, H. and Wang, X.F. (2022). High‐dimensional feature selection in competing risks modeling: A stable approach using a split‐and‐merge ensemble algorithm. Biometrical Journal. doi.org/10.1002/bimj.202100164.

Yue, Y., Bolin, D., Rue, H., and Wang, X.F. (2019). Bayesian Generalized Two-way ANOVA Modeling for Functional Data Using INLA. Statistica Sinica, in press.

Wang, X.F. and Xu, Y. (2017) Fast clustering using adaptive density peak detection. Statistical Methods in Medical Research, 26(6), 2800-2811.

Yue, Y.R. and Wang, X.F. (2016). Bayesian inference for generalized linear mixed models with multiple predictors subject to detection limits. Statistics in Medicine, 35(10):1689-705.

Faraway, J., Mahabal, A., Sun, J., Wang, X.F., Wang, Y., Zhang, L. (2016) Modeling light curves for improved classification of astronomical objects. Statistical Analysis and Data Mining. 9(1), 1-11.

Wang, X.F., and Ye, D. (2015). Conditional density estimation in measurement error problems. Journal of Multivariate Analysis, 133:38-50.

Sattar, A., Sinha, S.K., Wang, X.F. and Li, Y. (2015). Frailty models for pneumonia to death with a left-censored covariate, Statistics in Medicine. 34(14):2266-2280.

Huang E.P., Wang X.F., Roy Choudhury K, McShane L.M., Gönen M., Ye J., Buckler A.J., Kinahan P.E., Reeves A.P., Jackson E.F., Guimaraes A.R., Zahlmann G. (2015). Meta-analysis of the technical performance of an imaging procedure: Guidelines and statistical methodology. Statistical Methods in Medical Research. 24(1):141-74.

Mazzone, P., Wang, X.F., Xu, Y., Mekhail, T., Beukemann, M.C. Na, J., Kemling, J.W., Suslick, K.S., Sasidhar, M. (2012). Exhaled breath analysis with a colorimetric sensor array for the identification and characterization of lung cancer. Journal of Thoracic Oncology, 7(1): 137-142.

Wang, X.F., Jiang, Z., Daly, J.J. and Yue, G.H. (2012). A generalized regression model for region of interest analysis of fMRI data. NeuroImage, 59(1), 502-510.

Wang, X.F. and Wang, B. (2011). Deconvolution estimation in measurement error models: The R package decon. Journal of Statistical Software, 39(10), 1-24.