Article

Evaluation of Domain Generalization and Adaptation on Improving Model Robustness to Temporal Dataset Shift in Clinical Medicine

2021-06-22

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Importance</h4> Temporal dataset shift associated with changes in healthcare over time is a barrier to deploying machine learning-based clinical decision support systems. Algorithms that learn robust models by estimating invariant properties across time periods for domain generalization (DG) and unsupervised domain adaptation (UDA) might be suitable to proactively mitigate dataset shift. <h4>...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 7c35d56a-f3ed-500d-945c-f023d3ca3f51
DOI: 10.1101/2021.06.17.21259092

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Evaluation of Domain Generalization and Adaptation on Improving Model Robustness to Temporal Dataset Shift in Clinical MedicineDOI 10.1101/2021.06.17.21259092Read

Evaluating Deep Learning Sepsis Prediction Models in ICUs Under Distribution Shift: A Multi-Centre Retrospective Cohort StudySemantic proximity Read

EHR Foundation Models Improve Robustness in the Presence of Temporal Distribution ShiftSemantic proximity Read

Performance drift is a major barrier to the safe use of machine learning in cardiac surgerySemantic proximity Read

Local retraining mitigates domain shift in sepsis prediction: Lessons from translating a neonatal model to mixed intensive care dataSemantic proximity Read

A Foundation Model for Intensive Care: Unlocking Generalization across Tasks and Domains at ScaleSemantic proximity Read

Prospective Multicenter Validation of Machine Learning Models for Mortality Prediction in Adult Critically Ill Patients using Transfer LearningSemantic proximity Read

Calibrated and Interpretable Machine Learning for ICU Mortality Prediction Using First 24-Hour Clinical DataSemantic proximity Read

Diagnosing and remediating harmful data shifts for the responsible deployment of clinical AI modelsSemantic proximity Read

Observation-process features are associated with larger domain shift in sepsis mortality prediction: a cross-database evaluation using MIMIC-IV and eICU-CRDSemantic proximity Read

A Systematic Review of Cross-Population Shifts in Medical Imaging Analysis with Deep LearningSemantic proximity Read

Machine Learning based COVID-19 Diagnosis from Blood Tests with Robustness to Domain ShiftsSemantic proximity Read

Generalisability of AI-based scoring systems in the ICU: a systematic review and meta-analysisSemantic proximity Read