AIMI Journal Club: Development and Validation of an Interpretable Neural Network for Prediction of Postoperative In-Hospital Mortality - Christine Lee

Stanford community & AIMI affiliates only

Paper:

Lee, C.K., Samad, M., Hofer, I. et al. Development and validation of an interpretable neural network for prediction of postoperative in-hospital mortality. npj Digit. Med. 4, 8 (2021). https://doi.org/10.1038/s41746-020-00377-1

About:

Christine Lee, PhD is an engineer with more than 5 years of experience in applying data science and machine learning in the healthcare industry. She is passionate about creating technologies that provide meaningful clinical decision support and improve patient outcomes. She graduated with her PhD from UC Irvine in biomedical engineering under co-advisors Pierre Baldi and Maxime Cannesson where her research focused on applying deep learning to perioperative data, and she previously worked at Edwards Lifesciences as an applied machine learning engineer for critical care and anesthesiology medical devices. She is currently a data scientist at Brightside Health focused on helping to improve remote mental health.

Contact Email: 
aimicenter@stanford.edu

More Information: 
Link to paper

This event belongs to the following series

AIMI Journal Club