ArticlePatterns (New York, N.Y.)2021
Contrastive learning improves critical event prediction in COVID-19 patients.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Online Sepsis Prediction Using Vital Signs and Multiscale Temporal-Aware Contrastive Learning: Model Development and Validation Study.JMIR medical informatics · 2026Article
- Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data.BMC medical informatics and decision making · 2024Article
- LATTE: Label-efficient incident phenotyping from longitudinal electronic health records.Patterns (New York, N.Y.) · 2024Article
- Contrastive learning with transformer for adverse endpoint prediction in patients on DAPT post-coronary stent implantation.Frontiers in cardiovascular medicine · 2024Article
- Distance Learning in Nursing Education during the COVID-19 Pandemic: Psychosocial Impact for the Greek Nursing Students-A Qualitative Approach.Healthcare (Basel, Switzerland) · 2023Article
- Prediction of Acid-Base and Potassium Imbalances in Intensive Care Patients Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
- Continuous diagnosis and prognosis by controlling the update process of deep neural networks.Patterns (New York, N.Y.) · 2023Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- Sex trouble: Sex/gender slippage, sex confusion, and sex obsession in machine learning using electronic health records.Patterns (New York, N.Y.) · 2022Review
- Supervised Pretraining through Contrastive Categorical Positive Samplings to Improve COVID-19 Mortality Prediction.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2022Article
- Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort.Journal of clinical medicine · 2022Article
- Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data.The Lancet. Digital health · 2022Article
- Contrastive learning of heart and lung sounds for label-efficient diagnosis.Patterns (New York, N.Y.) · 2022Article
- SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records.Proceedings. IEEE International Conference on Data Mining · 2021Article
- FragNet, a Contrastive Learning-Based Transformer Model for Clustering, Interpreting, Visualizing, and Navigating Chemical Space.Molecules (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
Deep learning (DL) 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 DL models for the coronavirus disease 2019 (COVID-19) pandemic, where data are highly class imbalanced. Conventional approaches in DL use cross-entropy loss (CEL), which often suffers from poor margin classification. We show that contrastive loss (CL) improves the performance of CEL, especially in imbalanced electronic health records (EHR) data for COVID-19 analyses. We use a diverse EHR dataset to predict three outcomes: mortality, intubation, and intensive care unit (ICU) transfer in hospitalized COVID-19 patients over multiple time windows. To compare the performance of CEL and CL, models are tested on the full dataset and a restricted dataset. CL models consistently outperform CEL models, with differences ranging from 0.04 to 0.15 for area under the precision and recall curve (AUPRC) and 0.05 to 0.1 for area under the receiver-operating characteristic curve (AUROC).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.