On April 12, 2024, Dr. Huang Jidong, a Distinguished Professor at the School of Economics at Yunnan University and a Changjiang Lecture Professor appointed by the Ministry of Education, delivered a lecture titled "Application of Machine Learning Methods in Healthcare to Promote Public Health: A Scoping Review" at Peking University's Institute for Global Health and Development (GHD). Professor Liu Guoen, Director of the GHD and a distinguished professor at the National School of Development at Peking University, chaired the lecture. Some faculty and students from Peking University's GHD, School of Economics, and School of Public Health participated in the exchange and discussion.

Professor Huang Jidong provided a systematic overview covering various aspects of machine learning, including its background and applications in medicine and public health. The background knowledge of machine learning focused on distinguishing and connecting machine learning with artificial intelligence, different types of machine learning and their application scenarios, as well as various classic algorithms and their basic principles. The presentation included using ChatGPT as an example. Meanwhile, the rise of machine learning in medicine is primarily attributed to the widespread adoption of electronic health records. Over the years, machine learning has gradually been utilized for assisting diagnosis and treatment, discovering and developing new drugs, and conducting genome-wide association studies. Machine learning can also be applied in public health to predict the spread of infectious diseases such as COVID-19, prevent suicides, forecast the risk of alcohol use disorders, predict neonatal mortality rates, and anticipate childhood obesity, among others. It holds particular promise in identifying risk factors associated with non-communicable diseases, monitoring and supervising health behaviors, monitoring social and commercial determinants of health issues, and assisting in formulating and evaluating evidence-based interventions and policies.

In his conclusion, Professor Huang pointed out that while machine learning methods have been widely applied in medicine, their application in public health has not been fully realized. He emphasized the potential for machine learning to improve public health in areas such as disease prevention, health behaviors, social and commercial determinants of health, population-level interventions, and policymaking in the future.

(Interpreted by Waverly Shi)