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Feature/News

Methods Seminar- Alex Dubov, PhD on Machine Learning in Public Health Research: A Practical Introduction

Alex Dubov, PhD
Assistant Professor
Loma Linda University, Bioethics and Public Health
CHIPTS Combination Prevention Core Scientist
Tuesday, October 8th, 2pm-3pm

Artificial Intelligence is being called the new electricity—a technological invention that promises to transform our lives and the world. The resurgence of investment and enthusiasm for artificial intelligence, or the ability of machines to carry out “smart” tasks, is driven largely by advancements in the subfield of machine learning. Machine learning algorithms can analyze large volumes of complex data to find patterns and make predictions, often exceeding the accuracy and efficiency of people who are attempting the same task. Driven by tremendous growth in data collection and availability as well as computing power and accessibility, artificial intelligence and machine learning applications are rapidly growing in public health.

This presentation reviewed emerging application and implications of machine learning in public health research. The aim of this seminar was to increase participants’ understanding of machine learning, its relevance to public health research and practical challenges to its application, so as to enable participants to work in conjunction with people with technical skills in machine learning. We outlined what can and cannot be solved with machine learning models and provide a basic overview of machine learning techniques and their use.

The CHIPTS’ Methods Core hosts a monthly seminar series, which are one-hour workshops on research and statistical methods.  The seminars are open to HIV researchers, faculty, students, and community. To see previous seminars, check out the Methods Seminar tag or you can find seminar videos on our Youtube Channel! This series is hosted by the Center for HIV Identification, Prevention, and Treatment Services (CHIPTS) and made possible by funds from the National Institute of Mental Health (MH058107).

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