Hey, I'm Yikuan Li

I am currently a Ph.D. student 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 the disparities in access to care, through the analysis of heterogeneous real-word health data. I am fortunate to be advised by Dr. Yuan Luo, and have Dr. Sadiya Khan and Dr. Justin Starren on my thesis committee. I am also the awardee of the American Heart Association Predoctoral Fellowship. [My Résumé]

EDUCATION

09/2019 - 12/2024 Ph.D. in Health and Biomedical Informatics Northwestern University

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

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

PROFESSIONAL EXPERIENCE

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

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

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

TEACHING EXPERIENCE

09/2022 - 09/2023 Teaching Certificate Program Northwestern

05/2021 Teaching Assistant Excellence Award Northwestern

01/2023 - 04/2023 HSIP 442 Health and Biomedical Informatics Methods II Northwestern

01/2022 - 04/2022 Pub_Hlth 425 Intro to GIS for Public Health Northwestern

01/2021 - 04/2021 Pub_Hlth 425 Intro to GIS for Public Health Northwestern

01/2019 - 04/2019 EECS 495-78 Deep Learning from Scratch Northwestern

04/2018 - 07/2018 EECS 495-81 Reinforcement Learning from Scratch Northwestern

01/2018 - 04/2018 EECS 495-73 Optimization Techniques for Deep Learning Northwestern

09/2016 - 01/2017 EE375 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

Grants & Fellowships

2022 Predoctoral Fellowship ($65,106) American Heart Association

2016 Google Student Innovation Project Grant (¥12,000) Google

PUBLICATIONS

Li Y, Wehbe RM, Ahmad FS, Wang H, Luo Y. A comparative study of pretrained language models for long clinical text. Journal of the American Medical Informatics Association. 2023 Feb 1;30(2):340-7.
Yu Zheng†, Li Y †, Kim J, Huang K, Luo Y and Wang M. " Deep Reinforcement Learning for Cost-Effective Medical Diagnosis". International Conference on Learning Representations (ICLR) 2023
Li, Y, Hanyin Wang, and Yuan Luo. "Improving fairness in the prediction of heart failure length-of-stay by integrating social determinants of health." Circulation: Heart Failure. 2022; 15:1048–1056.
Li, Y, Wang H, and Luo Y. "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, 2020.
Li, Y, Yao L, Mao C, Srivastava A, Jiang X, and Luo Y. "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, 2018.
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. arXiv preprint arXiv:2309.08560. (Submitted to ICLR 2024)
Mao C, Xu J, Rasmussen L, Li Y, Adekkanattu P, Pacheco J, Bonakdarpour B, Vassar R, Shen L, Jiang G, Wang F. AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease. Journal of Biomedical Informatics. 2023 Aug 1;144:104442.
Wang H, Li Y, Hutch MR, Kline AS, Otero S, Mithal LB, Miller ES, Naidech A, Luo Y. 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. 2023 May 1;30(5):923-3
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.
Kline, Adrienne, Wang H, Li Y, Dennis S, Hutch M, Xu X, Wang F, Cheng F, and Luo Y. " Multimodal machine learning in precision health: A scoping review.". npj Digital Medicine 5, 171 (2022).
Zhao, Yiqing, Yu Y, Hanyin W, Li Y, Deng Y, Jiang G, and Luo Y. "Machine Learning in Causal Inference: Application in Pharmacovigilance." Drug Safety 45, no. 5 (2022): 459-476.
Wang, Hanyin, Li Y, Naidech A, and Luo Y. "Comparison between machine learning methods for mortality prediction for sepsis patients with different social determinants." BMC medical informatics and decision making 22, no. 2 (2022): 1-13.
Shin, Jiyoung, Li Y, and Luo Y. "Early Prediction of Mortality in Critical Care Setting in Sepsis Patients Using Structured Features and Unstructured Clinical Notes." 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021.
Wang, Hanyin, Li Y, Hutch M, Naidech A, and Luo Y. "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, no. 2 (2021): e26302.
Wang, H, Li Y, Khan SA, and Luo Y. "Prediction of breast cancer distant recurrence using natural language processing and knowledge-guided convolutional neural network." Artificial intelligence in medicine 110 (2020): 101977.
Wang, H, Li Y, Ning H, Wilkins J, Llyod-Jones D, Luo Y. "Using Machine Learning to Integrate Socio-Behavioral Factors in Predicting Cardiovascular-Related Mortality Risk." Studies in health technology and informatics. 264, 433-437

† Authors contributed equally