(1) 2020 - Doctor of Philosophy (PhD), Statistical epidemiology, The University of Manchester (2017 - 2020)
(2) 2017 - Master in Science (MSc), Health data science, The University of Manchester (2016 - 2017)
(3) 2013 - Bachelor of Science (BSc), Mathematics and applied mathematics, Sichuan University (2009 - 2013)
2021-1 to Now, Lecturer, School of Mathematical Sciences, Xiamen University
Yan is a statistician and applied statistician with PhD in statistical epidemiology, MSc in health data science and BSc degree in mathematics. Yan is also an experienced statistical programmer as he has worked in a data management company and has conducted extensive statistical analysis in his MSc and PhD project for 6 years. He has expertise using electronic health records (EHR) to conduct epidemiological studies. He is also experienced in conducting statistical analysis and fulfilling the Food and Drug Administration (FDA) requirements for clinical trials of new medicines as he conducted multiple projects in the analyses of trial data. His current research area focuses on assessing the generalisability of risk prediction models (including traditional risk prediction model and machine-learning (AI) models) using EHRs from UK databases.
Yan is recruiting master students who are interested in applied statistics, statistical programming, statistical epidemiology, health data science, clinical risk prediction model and machine learning.
If you are interested, please feel free to drop an email anytime. firstname.lastname@example.org
Introduction of the master program with Dr. Yan Li
Within the master program hosted by Mathematic academy of Xiamen University, the master student is expected to pass all the required master courses and successfully defend their master dissertation to be award the master-degree. To do so, the master students are expected to take the mandatory master courses while working on their master project which is designed and guided by their supervisor. The master project is normally a real-life scientific research project with amount of public interests while the supervisor is the expert in such domain.
Working with Dr. Yan Li in the master program, you will be directed to either antibiotic over-prescribing program or Clinical risk prediction model program (i.e., your master project would be derived from one of the programs). Antibiotic over-prescribing is a global concern as it increases antimicrobial resistance (i.e., the more antibiotics you use, the less effective these antibiotics would be). Statistical models and machine learning models are used to study prescription behavior to help reduce the number of antibiotics being prescribed. Clinical risk prediction models are mathematical/statistical/machine learning models being used in clinical settings to help clinicians and patients in clinical decision making. For example, Clinical risk prediction model such as QRISK3 was developed to help prevent cardiovascular disease (CVD), which is the leading cause of death around the world for decades. Further improving generalisability and clinical utility of these models is of interest in the current research area.
Yan is also open to self-proposed research project if the student has a strong feeling or interests to do so.
Visions and Expectations
Yan's lab is research focused. This means master students like you should have some level of general interests in science and research, and you will be trained on research skills and being participated in real-world research project since day 1. The lab is built on global standard which suggests you will be training on writing essays and presenting in English. Despite of research skills, the lab offers opportunity to develop other skills such as general working skills and programming skills that will be useful in your career. The student is expected to work on his/her master project while not taking classes. The group meeting will be held weekly to discuss progress and results.
It is advised to take Yan's class to know him if you are undergraduate students in XMU or have a casual chat with him before applying.
2021, Lecture “Practical application of statistical model and machine learning” in Xiamen University
2021, Lecture “Evaluation of risk prediction models in Learning Healthcare System (LHS)”in University College London (UCL)
2020, Lecture “Evaluate generalisability and clinical utility of risk prediction model” in University College London (UCL)
2020, Lecture “Evaluate risk prediction models in clinical risk prediction with electronic health records” in University of Manchester (UOM)
1. Y L, M S, DM A, TP van S. Consistency of ranking was evaluated as new measure for prediction model stability: longitudinal cohort study. J Clin Epidemiol. 2021;138:168-177. doi:10.1016/J.JCLINEPI.2021.06.026
2. Li Y, Sperrin M, Ashcroft DM, Van Staa TP. Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: Longitudinal cohort study using cardiovascular disease as exemplar. BMJ. 2020;371. doi:10.1136/bmj.m3919
3. Van Staa TP, Palin V, Li Y, et al. The effectiveness of frequent antibiotic use in reducing the risk of infection-related hospital admissions: Results from two large population-based cohorts. BMC Med. 2020;18(1):40. doi:10.1186/s12916-020-1504-5
4. Li Y, Sperrin M, Martin GP, Ashcroft DM, van Staa TP. Examining the impact of data quality and completeness of electronic health records on predictions of patients’ risks of cardiovascular disease. Int J Med Inform. Published online November 2019:104033. doi:10.1016/j.ijmedinf.2019.104033
5. Li Y, Sperrin M, Belmonte M, Pate A, Ashcroft DM, van Staa TP. Do population-level risk prediction models that use routinely collected health data reliably predict individual risks? Sci Rep. 2019;9(1):11222. doi:10.1038/s41598-019-47712-5
6. Li Y, Mölter A, White A, et al. Relationship between prescribing of antibiotics and other medicines in primary care: a cross-sectional study. Br J Gen Pract. 2019;69(678):e42-e51. doi:10.3399/bjgp18X700457
7. Li Y, Sperrin M, van Staa T. R package “QRISK3”: an unofficial research purposed implementation of ClinRisk’s QRISK3 algorithm into R. F1000Research. 2020;8:2139. doi:10.12688/f1000research.21679.3
He taught new employee SAS programing in clinical trials
He supervised master students for their dissertation
He taught master students how to program with R