Disentangled Representations Learning for Covid-19 Sequelae Prediction
thesisposted on 19.12.2021, 16:28 by Zhaorui LiuZhaorui Liu
Severe acute respiratory syndrome (SARS)-CoV-2 emerged in late 2019, then became an unprecedented public health crisis. Hundreds of millions of people have been affected. What is worse, many researchers have revealed that COVID-19 may have long-term effects on varieties of organs even after recovery. Consequently, there is a need for the study of its sequelae. The purpose of this project is to use machine learning algorithms to study the relationship between patients’ EMR data and long-term sequelae, especially kidney diseases. Inspired by a recent learning disentangled representation for recommendation work, this project proposes a method that (i) predicts the development trend of the kidney disease; (ii) learn representations that uncover and disentangle factors related to kidney diseases. The major contribution is that this model has high interpretability which enables medical works to infer the development of patients' condition.