Article

Machine learning outperforms state-of-the-art continuous vital sign monitoring

2026-01-20

Abstract excerpt

<title>Abstract</title> <p>Continuous Vital Sign Monitoring (CVSM) may allow early detection of patient deterioration with clinical impact from reduced complications. CVSM-based alert algorithms are vulnerable to missing data and artefacts, where large numbers of false positive alerts impose alert fatigue among healthcare staff, especially in low-staffed environments such as general wards. An unexplored option fo...

Topics

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

Identifiers and source

Literature Corpus work: 63e21ec1-8dbd-55eb-b1ce-e01e656f972b
DOI: 10.21203/rs.3.rs-8436885/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Machine learning outperforms state-of-the-art continuous vital sign monitoringDOI 10.21203/rs.3.rs-8436885/v1Read

Improved Inpatient Deterioration Detection in General Wards by Using Time-Series Vital SignsSemantic proximity Read

Improved Sensitivity For Detection Of Clinical Deterioration When Diagnostic Pathology And Patient Trends Are Included In Machine Learning ModelsSemantic proximity Read

Beyond episodic early warning systems: a continuous clinical alert system for early detection of in-hospital deteriorationSemantic proximity Read

Development and Prospective Validation of Predictive Model for Early Hemodynamic Deterioration in Critical Care: A Multicenter StudySemantic proximity Read

Algorithm Refinement of the National Early Warning Score 2 (NEWS2): Study ProtocolSemantic proximity Read

Prediction of Hypotension Events with Physiologic Vital Sign Signatures in The Intensive Care UnitSemantic proximity Read

Development and Validation of VC-MAES and VC-SEPS: Deep Learning-Based Early Warning Systems for Hospitalized PatientsSemantic proximity Read

A Multicenter Validation Study of the Deep Learning-based Early Warning Score for Predicting in-hospital Cardiac Arrest in Patients Admitted to General WardsSemantic proximity Read

ADVISE: A Machine Learning Framework for Early Recognition of a Surrogate Marker for Ventilator-Associated Pneumonia Using Routinely Collected Critical Care DataSemantic proximity Read

Derivation and Validation of a Clinical Deterioration Early Warning System (Cdews) Score in Hospital InpatientsSemantic proximity Read

Low Responsiveness of Machine Learning Models to Critical or Deteriorating Health ConditionsSemantic proximity Read

Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signaturesSemantic proximity Read