教育经历：

(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)

工作经历：

2021-1 to Now, Lecturer, School of Mathematical Sciences, Xiamen University

研究方向：

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.

课题组招生说明：

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. [email protected]

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 successfully defend their master dissertation and pass all the required master courses to be award the master-degree. To do so, the master students are expected to work hard on agreed master project which is designed and guided by the supervisor while taking the mandatory master courses which might be useful for the research project. 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 strong will and can present it with evidence.

Role as master students

The general role as master students is to promote yourself to the degree level of master with the guidance and help from your supervisor, the key difference to the undergraduate level is that you are expected to be more self-motivated and more prone to work (i.e., we are expecting more output than input). This means you are mainly expected to:

1. Work to achieve enough research output that would satisfy graduation criteria.

2. Pass the master courses that required by school, academy and supervisor.

3. Finish other required academic tasks or school/academy events.

4. Independently defend your thesis in the final Viva.

Role as supervisor

The role of supervisor is to nurture you to achieve the degree of master, this means your supervisor is not just an examiner but a role to provide guidance, help and criticism to promote you to the next academic level. Unfortunately, your supervisor would not be allowed to present in your final viva where you need to defend your master thesis with accomplished research output, while facing foreseen challengeable questions from at least two reviewers intensively. Therefore, the supervisor, who had these experience before, would use numerous approaches to train you in multiple aspects, so you may successfully defend the thesis in the viva.

Visions and Expectations

Yan’s lab is research focused. We expect you to have some general level of interests in science or may be developed later. You will be trained on research skills and being participated in real-world research project since day 1. The lab is built internationally, which means 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 programming that will be useful in your career. The student is expected to work on our agreed master project as daily routine 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 get to know him especially if you are undergraduate students from XMU or just have a casual chat anytime if you like.

授课情况：

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)

论文：

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

学生培养：

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