Chang Su

Assistant Professor
Health Services Administration and Policy
Office
1301 Cecil B. Moore Avenue Ritter Annex Room 535

Biography

Dr. Su is currently an assistant professor in health informatics in the Department of Health Services Administration and Policy. He obtained his PhD in system engineering and sciences with a focus on network data mining from Xi'an Jiaotong University, China. Before joining Temple University, Dr. Su had been a postdoctoral associate at the Division of Health Informatics in the Department of Healthcare Policy and Research at the Weill Cornell Medical College, Cornell University.

Dr. Su's research, lying in the intersection between data science and medicine, aims at developing novel computational approaches (e.g., machine learning and deep learning methods) to derive new insights from the multi-modal health data (including Electronic Health Records (EHRs), multi-omics data, medical imaging data, socioeconomics data, etc.) towards accelerating human health study. Dr. Su is also interested in bridging the gap between medical experts and data-driven approaches using the biomedical knowledge graph. The areas that Dr. Su is specifically interested in include but are not limited to:

  • Multi-modal modeling for data-driven subtyping to disentangle heterogeneous symptom progression patterns and uncover biological mechanisms of chronic human health conditions like Alzheimer’s disease and Parkinson’s disease.
  • Machine learning-based predictive modeling in mental health, especially suicide attempt prediction, via integrative modeling of multiple data resources like EHR data, claim data, and socioeconomics data, etc.
  • Curation of comprehensive or disease-specific biomedical knowledge graphs and development of graph-based learning methods like graph embedding and deep graph neural networks for knowledge discovery, e.g., in silico drug repurposing.
  • Incorporation of biomedical knowledge graph in accelerating health data modeling, e.g., EHR data.

Education

  • PhD, Control Science and Engineering, Xi’an Jiaotong University
  • BE, Automation, Xi’an Jiaotong University

Curriculum Vitae 

Courses Taught

Number

Name

Level

HIM 5128

Health Data: Standards and Interoperability

Graduate

HIM 5299

Introduction to Language Processing and Text Mining for Health Professionals

Graduate

HIM 8216

Applications of Machine Learning for Health Informatics

Graduate

Selected Publications

  • Su, C., Hou, Y.u., Zhou, M., Rajendran, S., Maasch, J.R., Abedi, Z., Zhang, H., Bai, Z., Cuturrufo, A., Guo, W., Chaudhry, F.F., Ghahramani, G., Tang, J., Cheng, F., Li, Y., Zhang, R., DeKosky, S.T., Bian, J., & Wang, F. (2023). Biomedical discovery through the integrative biomedical knowledge hub (iBKH). IScience, 26(4), p. 106460. United States. doi: 10.1016/j.isci.2023.106460

  • Brendel, M., Su, C., Bai, Z., Zhang, H., Elemento, O., & Wang, F. (2022). Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review. Genomics Proteomics Bioinformatics, 20(5), pp. 814-835. China. doi: 10.1016/j.gpb.2022.11.011

  • Xu, W., Su, C., Li, Y., Rogers, S., Wang, F., Chen, K., & Aseltine, R. (2022). Improving suicide risk prediction via targeted data fusion: proof of concept using medical claims data. J Am Med Inform Assoc, 29(3), pp. 500-511. England. doi: 10.1093/jamia/ocab209

  • Su, C., Zhang, Y., Flory, J.H., Weiner, M.G., Kaushal, R., Schenck, E.J., & Wang, F. (2021). Clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health. NPJ Digit Med, 4(1), p. 110. England. doi: 10.1038/s41746-021-00481-w

  • Cui, Y., Du, Y., Wang, X., Wang, H., & Su, C. (2021). Leveraging attention‐based visual clue extraction for image classification. IET Image Processing, 15(12), pp. 2937-2947. doi: 10.1049/ipr2.12280

  • Zhao, S., Su, C., Lu, Z., & Wang, F. (2021). Recent advances in biomedical literature mining. Brief Bioinform, 22(3). England. doi: 10.1093/bib/bbaa057

  • Su, C., Hou, Y.u., Rajendran, S., Maasch, J.R., Abedi, Z., Zhang, H., Bai, Z., Cuturrufo, A., Guo, W., Chaudhry, F.F., Ghahramani, G., Tang, J., Cheng, F., Li, Y., Zhang, R., Bian, J., & Wang, F. (2021). Biomedical Discovery through the integrative Biomedical Knowledge Hub (iBKH). doi: 10.1101/2021.03.12.21253461

  • Su, C., Hoffman, K., Zhenxing, X.u., Sanchez, E., Siempos, I., Harrington, J.S., Racanelli, A., Plataki, M., Wang, F., & Schenck, E.J. (2021). Evaluation of albumin kinetics in mechanically ventilated patients with COVID-19 compared to those with sepsis-induced ARDS. doi: 10.1101/2021.03.16.21253405

  • Su, C., Zhang, Y., Flory, J.H., Weiner, M.G., Kaushal, R., Schenck, E.J., & Wang, F. (2021). Novel clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health. doi: 10.1101/2021.02.28.21252645

  • Xu, J., Glicksberg, B.S., Su, C., Walker, P., Bian, J., & Wang, F. (2021). Federated Learning for Healthcare Informatics. J Healthc Inform Res, 5(1), pp. 1-19. Switzerland. doi: 10.1007/s41666-020-00082-4

  • Zhu, Y., Che, C., Jin, B.o., Zhang, N., Su, C., & Wang, F. (2020). Knowledge-driven drug repurposing using a comprehensive drug knowledge graph. Health Informatics J, 26(4), pp. 2737-2750. England. doi: 10.1177/1460458220937101

  • Su, C., Aseltine, R., Doshi, R., Chen, K., Rogers, S.C., & Wang, F. (2020). Machine learning for suicide risk prediction in children and adolescents with electronic health records. Transl Psychiatry, 10(1), p. 413. United States. doi: 10.1038/s41398-020-01100-0

  • Su, C., Xu, Z., Pathak, J., & Wang, F. (2020). Deep learning in mental health outcome research: a scoping review. Transl Psychiatry, 10(1), p. 116. United States. doi: 10.1038/s41398-020-0780-3

  • Pan, W., Su, C., Chen, K., Henchcliffe, C., & Wang, F. (2020). Learning Phenotypic Associations for Parkinson’s Disease with Longitudinal Clinical Records. doi: 10.1101/2020.03.15.20036657

  • Su, C., Tong, J., Zhu, Y., Cui, P., & Wang, F. (2020). Network embedding in biomedical data science. Brief Bioinform, 21(1), pp. 182-197. England. doi: 10.1093/bib/bby117

  • Wang, H., Du, Y., Zhang, G., Cai, Z., & Su, C. (2020). Learning Fundamental Visual Concepts Based on Evolved Multi-Edge Concept Graph. IEEE Transactions on Multimedia. doi: 10.1109/TMM.2020.3042072

  • Su, C., Tong, J., & Wang, F. (2020). Mining genetic and transcriptomic data using machine learning approaches in Parkinson's disease. NPJ Parkinsons Dis, 6, p. 24. United States. doi: 10.1038/s41531-020-00127-w

  • Zhao, S., Su, C., Sboner, A., & Wang, F. (2019). GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment
    Share on. Proceedings of the 28th ACM International Conference on Information and Knowledge Management. CIKM '19: The 28th ACM International Conference on Information and Knowledge Management: ACM. doi: 10.1145/3357384.3358038

  • Du, Y., Wang, X., Cui, Y., Wang, H., & Su, C. (2019). Kernel-Based Mixture Mapping for Image and Text Association. IEEE Transactions on Multimedia, 22(2), pp. 365-379. doi: 10.1109/tmm.2019.2930336

  • Wang, X..., Du, Y..., Li, X..., Cao, F..., & Su, C... (2019). Embedded Representation of Relation Words with Visual Supervision. 2019 Third IEEE International Conference on Robotic Computing (IRC), pp. 409-412. 2019 Third IEEE International Conference on Robotic Computing (IRC).

  • Zhang, X.i.S., Chen, D., Zhu, Y., Che, C., Su, C., Zhao, S., Min, X.u., & Wang, F. (2018). A Multi-View Ensemble Classification Model for Clinically Actionable
    Genetic Mutations.     Retrieved from http://arxiv.org/

  • Su, C., Guan, X., Du, Y., Wang, Q., & Wang, F. (2018). A fast multi-level algorithm for community detection in directed online social networks. JOURNAL of INFORMATION SCIENCE, 44(3), pp. 392-407. doi: 10.1177/0165551517698305

  • Su, C., Guan, X., Du, Y., Huang, X., & Zhang, M. (2018). Toward capturing heterogeneity for inferring diffusion networks: A mixed diffusion pattern model. Knowledge-Based Systems, 147, pp. 81-93. doi: 10.1016/j.knosys.2018.02.017