Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients

no code implementations • 11 Jan 2021 • Tingyi Wanyan, Hossein Honarvar, Suraj K. Jaladanki, Chengxi Zang, Nidhi Naik, Sulaiman Somani, Jessica K. De Freitas, Ishan Paranjpe, Akhil Vaid, Riccardo Miotto, Girish N. Nadkarni, Marinka Zitnik, ArifulAzad, Fei Wang, Ying Ding, Benjamin S. Glicksberg

This has been a major issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, particularly within electronic health records (EHR) research.

Clustering Contrastive Learning +1

Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records

no code implementations • 28 Dec 2020 • Tingyi Wanyan, Hossein Honarvar, Ariful Azad, Ying Ding, Benjamin S. Glicksberg

In this work, we train a Heterogeneous Graph Model (HGM) on Electronic Health Record data and use the resulting embedding vector as additional information added to a Convolutional Neural Network (CNN) model for predicting in-hospital mortality.

Mortality Prediction