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Search results (286)

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Understanding how post-intensive care follow-up is delivered within the role of critical care outreach teams: a qualitative study protocol.

Bonner A. et al, (2026), BMJ Open, 16

Platelet transfusion thresholds for vascular access.

Shah A. et al, (2026), Anaesthesia, 81, 754 - 755

You Can't Be With Your Patients All the Time-Patient and Staff Views of a Wearable Vital Signs Monitoring System.

Edwards C. et al, (2026), J Adv Nurs

Opportunities to Improve Nutrition for Patients in Hospital After Discharge From an Intensive Care Unit: A Human Factors Analysis.

Vollam S. et al, (2026), Nurs Crit Care, 31

Screening for hypertension in the inpatient environment (SHINE): a prospective diagnostic accuracy study among adult hospital patients.

Armitage LC. et al, (2026), BMJ Open, 16

RETRACTED ARTICLE: DynaGraph: interpretable dynamic graph learning for temporal electronic health records.

Mesinovic M. et al, (2026), NPJ Digit Med, 9

DySurv: dynamic deep learning model for survival analysis with conditional variational inference.

Mesinovic M. et al, (2026), J Am Med Inform Assoc, 33, 112 - 122

Multivariable Prediction Models for Atrial Fibrillation after Cardiac Surgery: A Systematic Review and Critical Appraisal.

Fields KG. et al, (2025), Anesthesiology, 143, 1643 - 1655

Non-pharmacological post-intensive care interventions to improve patient outcome following critical illness: a scoping review.

Gustafson O. et al, (2025), Crit Care, 30

A retrospective records review comparing the care of patients who either avoided or were admitted to an ICU following a ward-based deterioration event.

Ede J. et al, (2025), Intensive Crit Care Nurs, 90

Oxygen therapy in early warning scores: a systematic review and meta-analysis.

Harrison CH. et al, (2025), Thorax, 80, 693 - 701

Development and external validation of a clinical prediction model for new-onset atrial fibrillation in intensive care: a multicentre, retrospective cohort study.

Bedford JP. et al, (2025), Lancet Digit Health

Maternal early warning scores shown to be methodologically weak and at high risk of bias.

Chester-Jones M. et al, (2025), J Clin Epidemiol, 184

Explainable machine learning for predicting ICU mortality in myocardial infarction patients using pseudo-dynamic data.

Mesinovic M. et al, (2025), Sci Rep, 15

Explainability in the age of large language models for healthcare.

Mesinovic M. et al, (2025), Commun Eng, 4

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