Hey, I'm Yikuan Li

Contact: yikuan.li@northwestern.edu

I am a Ph.D. candidate in the Health and Biomedical Informatics track of the HSIP program at Feinberg School of Medicine, Northwestern University. My research interest lies in leveraging artificial intelligence to improve health outcomes and mitigate health disparities through the analysis of heterogeneous real-word health data. I am fortunate to be advised by Dr. Yuan Luo, and to have Dr. Sadiya Khan and Dr. Justin Starren on my dissertation committee. I am also the awardee of the American Heart Association Predoctoral Fellowship. I am currently on the academic job market for a junior faculty position in health informatics. I would greatly appreciate it if you could reach out with any job opportunities or career advice.

EDUCATION

09/2019 - 06/2025 Ph.D. in Health and Biomedical Informatics Northwestern University

09/2017 - 05/2019 M.S in Electrical Engineering and Computer Science Northwestern University

09/2013 - 07/2017 B.S. in Information Engineering Shanghai Jiao Tong University

Grants & Fellowships

01/2023 - 06/2025 Predoctoral Fellowship ($65,106) American Heart Association

01/2015-06/2017 Google Student Innovation Project Grant (¥12,000) Google

PUBLICATIONS

1. Li, Y., Wang, H., Yerebakan, H. Z., Shinagawa, Y., & Luo, Y. (2024). FHIR-GPT Enhances Health Interoperability with Large Language Models. NEJM AI, AIcs2300301.
2. Li, Y., Wehbe, R. M., Ahmad, F. S., Wang, H., & Luo, Y. (2023). A comparative study of pretrained language models for long clinical text. Journal of the American Medical Informatics Association, 30(2), 340-347.
3. Li, Y., Wang, H., & Luo, Y. (2022). Improving fairness in the prediction of heart failure length of stay and mortality by integrating social determinants of health. Circulation: Heart Failure, 15(11), e009473.
4. Li, Y.†, Yu, Z.†, Kim, J., Huang, K., Luo, Y., & Wang, M. (2023). Deep reinforcement learning for cost-effective medical diagnosis. International Conference on Learning Representations (ICLR) 2023. († Co-first authors)
5. Li, Y., Wang, H., & Luo, Y. (2020, December). A comparison of pre-trained vision-and-language models for multimodal representation learning across medical images and reports. In 2020 IEEE international conference on bioinformatics and biomedicine (BIBM) (pp. 1999-2004). IEEE.
6. Li, Y., Yao, L., Mao, C., Srivastava, A., Jiang, X., & Luo, Y. (2018, December). Early prediction of acute kidney injury in critical care setting using clinical notes. In 2018 IEEE international conference on bioinformatics and biomedicine (BIBM) (pp. 683-686). IEEE.
7. Li, Y., Mao, C., Huang, K., Wang, H., Yu, Z., Wang, M., & Luo, Y. (2023). Deep reinforcement learning for efficient and fair allocation of health care resources. arXiv preprint arXiv:2309.08560. (Under-review by AAAI ’25)
8. Mao, C., Xu, J., Rasmussen, L., Li, Y., Adekkanattu, P., Pacheco, J., ... & Luo, Y. (2023). AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease. Journal of Biomedical Informatics, 144, 104442.
9. Wang, H., Li, Y., Hutch, M. R., Kline, A. S., Otero, S., Mithal, L. B., ... & Luo, Y. (2023). Patterns of diverse and changing sentiments towards COVID-19 vaccines: a sentiment analysis study integrating 11 million tweets and surveillance data across over 180 countries. Journal of the American Medical Informatics Association, 30(5), 923-931.
10. Pacheco JA, Rasmussen LV, …, Li Y., …, Liu C. Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network. Scientific reports. 2023 Feb 3;13(1):1971. 1.
11. Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., ... & Luo, Y. (2022). Multimodal machine learning in precision health: A scoping review. npj Digital Medicine, 5(1), 171.
12. Wang, H., Li, Y., Naidech, A., & Luo, Y. (2022). Comparison between machine learning methods for mortality prediction for sepsis patients with different social determinants. BMC medical informatics and decision making, 22(Suppl 2), 156.
13. Zhao, Y., Yu, Y., Wang, H., Li, Y., Deng, Y., Jiang, G., & Luo, Y. (2022). Machine learning in causal inference: Application in pharmacovigilance. Drug Safety, 45(5), 459-476.
14. Shin, J., Li, Y., & Luo, Y. (2021, December). Early prediction of mortality in critical care setting in sepsis patients using structured features and unstructured clinical notes. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2885-2890). IEEE.
15. Wang, H., Li, Y., Hutch, M., Naidech, A., & Luo, Y. (2021). Using tweets to understand how COVID-19–Related health beliefs are affected in the age of social media: Twitter data analysis study. Journal of medical Internet research, 23(2), e26302.
16. Wang, H., Li, Y., Khan, S. A., & Luo, Y. (2020). Prediction of breast cancer distant recurrence using natural language processing and knowledge-guided convolutional neural network. Artificial intelligence in medicine, 110, 101977.
17. Wang, H., Li, Y., Ning, H., Wilkins, J., Lloyd-Jones, D., & Luo, Y. (2019). Using machine learning to integrate socio-behavioral factors in predicting cardiovascular-related mortality risk. Studies in Health Technology Information. 2019 Aug 21;264:433-437.

CONFERENCE PRESENTATIONS

1. Li, Y., Khan S., & Luo, Y. Enhancing Heart Failure Mortality Prediction with Multimodal Machine Learning using Structured and Genotype Variables. AHA Scientific Sessions 2024, Chicago, IL. (Early Innovators Spotlight Oral Presentation)
2. Li, Y., Mao, C., Huang, K., Wang, H., Yu, Z., Wang, M., & Luo, Y. Deep reinforcement learning for efficient and fair allocation of health care resources. AMIA 2024 Annual Symposium, San Francisco, CA, 11/2024. (Podium Presentation)
3. Li, Y., Wang, H., Yerebakan, H. Z., Shinagawa, Y., & Luo, Y. FHIR-GPT Enhances Health Interoperability with Large Language Models. AMIA 2024 Informatics Summit, Boston, MA, 03/2024. (Podium Presentation)
4. Li, Y., Mao, C., Huang, K., Wang, H., Yu, Z., Wang, M., & Luo, Y. Deep reinforcement learning for efficient and fair allocation of health care resources. NeurIPS 2023, New Orleans, LA, 11/2024. (Poster)

INVITED TALKS AND SEMINARS

1. “Deep Reinforcement Learning for Health” Healthcare AI Forum, Northwestern Medicine, Chicago, IL, 02/2024.
2. “The Influence of Social Determinants of Health on Heart Failure”, American Hearth Association Webinars, Online, 03/2023.

PROFESSIONAL EXPERIENCE

06/2024 - 09/2024 LLM Research Intern Samsung Research America, Mountain View, CA

06/2023 - 09/2023 NLP Research Intern Siemens Medical Solutions, Malvern, PA

06/2019 - 09/2019 Consulting Intern IQVIA, Shanghai, China

06/2016 - 01/2017 Support Engineer Intern Microsoft, Shanghai, China

TEACHING EXPERIENCE

09/2022 - 09/2023 Teaching Certificate Program Northwestern

05/2021 Teaching Assistant Excellence Award Northwestern

01/2023 - 04/2023 Health and Biomedical Informatics Methods II Northwestern

01/2022 - 04/2022 Intro to GIS for Public Health Northwestern

01/2021 - 04/2021 Intro to GIS for Public Health Northwestern

01/2019 - 04/2019 Deep Learning from Scratch Northwestern

04/2018 - 07/2018 Reinforcement Learning from Scratch Northwestern

01/2018 - 04/2018 Optimization Techniques for Deep Learning Northwestern

09/2016 - 01/2017 Nonlinear Optics SJTU

ACADEMIC SERVICE

Journal Reviewer

1) Journal of the American Medical Informatics Association (JAMIA); 2) Journal of Biomedical Informatics (JBI); 3) Journal of Medical Internet Research (JMIR); 4) BMC Medical Informatics and Decision Making; 6) Scientific Reports; 7) BioData Mining; 8) Technology in Society; 9) JMIR Medical Informatics,; 10) JMIR mHealth and uHealth; 11) JMIR Medical Education; 12) JMIR Formative Research; 13) JMIR Public Health and Surveillance; 14) Heliyon; 15) Circulation: Heart Failure; 16) Frontier in Digital Health; 17) Biomed. Signal Processing; 18) Comput. Methods Programs Biomed. 19) International Journal of Medical Informatics; 20) BMC Supplements

Conference Reviewer