Please join us for the next installment of the MGH Biostatistics Applied Biostatistics Seminar Series, a new seminar series designed to introduce researchers to intermediate topics that are highly relevant to clinical biostatistical research. Presenters will join us on every 3rd Friday of each month to introduce us to their area of expertise and motivate the use of these methods with concrete clinical examples.
Using Neural Networks to Predict Breast Cancer Risk
Friday February 18, 2022, 1:00-2:00pm
https://partners.zoom.us/j/89183981161
Speaker: Zoe Guan, PhD, Postdoctoral Research Fellow, Memorial Sloan Kettering Cancer Center
Abstract: Neural networks are a flexible machine learning method inspired by the structure of the brain. They are able to capture complex nonlinear relationships between inputs and outputs and have achieved remarkable accuracy in many prediction and classification problems, including natural language processing, image recognition, and prediction of health outcomes. Recently, there has been growing interest in applying neural networks to clinical problems, especially with the acceleration of large-scale data collection efforts in healthcare. In this talk, I will give an overview of two types of neural networks, fully-connected and convolutional, and discuss an application of these approaches to breast cancer risk prediction using family history data.
This will be a virtual event. Please contact tthaweethai@mgh.harvard.edu with any questions.
Upcoming seminars:
Friday March 18, 2022 (1-2pm): Kaitlyn Cook, PhD, Postdoctoral Research Fellow, Harvard Medical School and Harvard Pilgrim Healthcare Institute. Topic: Interval-censored data and HIV prevention trials
