Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients

by   Tingyi Wanyan, et al.

Machine Learning (ML) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. 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. Conventional approaches in ML use cross-entropy loss (CEL) that often suffers from poor margin classification. For the first time, we show that contrastive loss (CL) improves the performance of CEL especially for imbalanced EHR data and the related COVID-19 analyses. This study has been approved by the Institutional Review Board at the Icahn School of Medicine at Mount Sinai. We use EHR data from five hospitals within the Mount Sinai Health System (MSHS) to predict mortality, intubation, and intensive care unit (ICU) transfer in hospitalized COVID-19 patients over 24 and 48 hour time windows. We train two sequential architectures (RNN and RETAIN) using two loss functions (CEL and CL). Models are tested on full sample data set which contain all available data and restricted data set to emulate higher class imbalance.CL models consistently outperform CEL models with the restricted data set on these tasks with differences ranging from 0.04 to 0.15 for AUPRC and 0.05 to 0.1 for AUROC. For the restricted sample, only the CL model maintains proper clustering and is able to identify important features, such as pulse oximetry. CL outperforms CEL in instances of severe class imbalance, on three EHR outcomes with respect to three performance metrics: predictive power, clustering, and feature importance. We believe that the developed CL framework can be expanded and used for EHR ML work in general.



There are no comments yet.


page 5

page 6

page 13

page 14

page 18

page 19

page 23

page 24


Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data

Electronic Health Record (EHR) data has been of tremendous utility in Ar...

Reinforcement Learning Assisted Oxygen Therapy for COVID-19 Patients Under Intensive Care

Patients with severe Coronavirus disease 19 (COVID-19) typically require...

SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records

Contrastive learning has demonstrated promising performance in image and...

Individualized Prediction of COVID-19 Adverse outcomes with MLHO

The COVID-19 pandemic has devastated the world with health and economic ...

Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression

Modeling a system's temporal behaviour in reaction to external stimuli i...

Benchmarking AutoML Frameworks for Disease Prediction Using Medical Claims

We ascertain and compare the performances of AutoML tools on large, high...

Classification supporting COVID-19 diagnostics based on patient survey data

Distinguishing COVID-19 from other flu-like illnesses can be difficult d...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.